Exploring Treatment x Trial Interactions using Treatment Stability/Trial Dendrogram plots
Peter Claussen
October 15, 2015
Here I use the CPT 2007 data set to illustrate new options avialable for ST Treatment Stability/Trial Dendrogram plots.
Multilocation
Using scripts from ARM ST to show options.
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
cbColors <- c(cbPalette,cbbPalette)
path = "../Manuscripts/scripts/multilocation"
means.vector <- read.delim('../Manuscripts/scripts/multilocation/trialMeans.SmyCol1.tab',header=FALSE)
means.matrix <- read.delim('../Manuscripts/scripts/multilocation/trialTable.SmyCol1.tab',header=FALSE)
means.vector <- means.vector[,1]
par(fig = c(0, 1, 0.4, 1), mar = (c(1, 6, 2, 1) + 0.1), ps = 12, cex.lab = 1.166667, cex.main = 1.333333, cex.axis = 1)
res1<-plot.interaction.ARMST(means.matrix, means.vector, ylab='Treatment in Trial Mean \nYield',regression=TRUE, main='Treatment Stability and Trial Clusters for Grand Mean 1', show.legend=TRUE,legend.columns=1, legend.pos=c(.01,.98),trt.colors=cbColors)
par(fig=c(0,1,0,.4),mar=(c(4, 6, 0, 1) + 0.1), new=TRUE)
res2<-plot.clusters.ARMST(means.matrix, means.vector, xlab='Trial Mean \nMultilocation', ylab='',trt.colors =cbColors)
par(fig = c(0, 1, 0, 1))path = "../Manuscripts/scripts/multilocation"
means.vector <- read.delim('../Manuscripts/scripts/multilocation/trialMeans.SmyCol1.tab',header=FALSE)
means.matrix <- read.delim('../Manuscripts/scripts/multilocation/trialTable.SmyCol1.tab',header=FALSE)
means.vector <- means.vector[,1]
par(fig = c(0, 1, 0.4, 1), mar = (c(1, 6, 2, 1) + 0.1), ps = 12, cex.lab = 1.166667, cex.main = 1.333333, cex.axis = 1)
res1<-plot.interaction.ARMST(means.matrix, means.vector, ylab='Treatment in Trial Mean \nYield',regression=TRUE, main='Treatment Stability and Trial Clusters for Grand Mean 1', show.legend=TRUE,legend.columns=1, legend.pos=c(.01,.98),trt.colors=cbColors)
par(fig=c(0,1,0,.4),mar=(c(4, 6, 0, 1) + 0.1), new=TRUE)
decomp <- decompose.means.table(means.matrix)
fg="black"
res2<-plot.clusters.ARMST(means.matrix, means.vector, fg=fg,xlab='Trial Mean \nMultilocation', ylab='',reference=(decomp$mu + decomp$alpha + decomp$beta),trt.colors=cbColors)
fg=cbColors[2]
res3 <- plot.clusters.ARMST(decomp$mu + decomp$alpha + decomp$beta, means.vector, fg=fg,add=TRUE,xlab='Trial Mean \nMultilocation', ylab='',trt.colors=cbColors)
par(fig = c(0, 1, 0, 1))str(res2$means.hc)## List of 7
## $ merge : int [1:8, 1:2] -6 -1 -5 -4 2 -2 1 6 -8 -3 ...
## $ height : num [1:8] 0.168 0.227 0.385 0.423 0.507 ...
## $ order : int [1:9] 2 4 9 6 8 1 3 5 7
## $ labels : NULL
## $ method : chr "complete"
## $ call : language hclust(d = dist(means.matrix), method = method)
## $ dist.method: chr "euclidean"
## - attr(*, "class")= chr "hclust"res2$means.hc$height## [1] 0.1680609 0.2272786 0.3849098 0.4228869 0.5068531 0.5913357 0.9790983
## [8] 1.9802638res3$means.hc$height## [1] 0.05000015 0.05500003 0.10166665 0.12000000 0.42166670 0.44333339
## [7] 0.96833340 1.94666672res2$means.hc$height/res3$means.hc$height## [1] 3.361208 4.132335 3.785999 3.524058 1.202023 1.333840 1.011117 1.017259res2$means.hc$order## [1] 2 4 9 6 8 1 3 5 7res3$means.hc$order## [1] 2 4 9 6 8 5 7 1 3res2$means.hc$merge## [,1] [,2]
## [1,] -6 -8
## [2,] -1 -3
## [3,] -5 -7
## [4,] -4 -9
## [5,] 2 3
## [6,] -2 4
## [7,] 1 5
## [8,] 6 7res3$means.hc$merge## [,1] [,2]
## [1,] -5 -7
## [2,] -4 -9
## [3,] -1 -3
## [4,] -6 -8
## [5,] 1 3
## [6,] -2 2
## [7,] 4 5
## [8,] 6 7res2$means.hc$merge==res3$means.hc$merge## [,1] [,2]
## [1,] FALSE FALSE
## [2,] FALSE FALSE
## [3,] FALSE FALSE
## [4,] FALSE FALSE
## [5,] FALSE TRUE
## [6,] TRUE FALSE
## [7,] FALSE TRUE
## [8,] TRUE TRUEmatched.idx <- compare.merges(res2$means.hc$merge,res3$means.hc$merge)
matched.idx## [1] 4 3 1 2 0 0 0 8res2$means.hc$height## [1] 0.1680609 0.2272786 0.3849098 0.4228869 0.5068531 0.5913357 0.9790983
## [8] 1.9802638res3$means.hc$height## [1] 0.05000015 0.05500003 0.10166665 0.12000000 0.42166670 0.44333339
## [7] 0.96833340 1.94666672res3$means.hc$height[matched.idx]## [1] 0.12000000 0.10166665 0.05000015 0.05500003 1.94666672The function cluster.stats() in the fpc package provides a mechanism for comparing the similarity of two cluster solutions using a variety of validation criteria (Hubert’s gamma coefficient, the Dunn index and the corrected rand index) # comparing 2 cluster solutions
library(fpc)
d <- dist(means.matrix)
cluster.stats(d, res2$clusters, res3$clusters)## $n
## [1] 9
##
## $cluster.number
## [1] 3
##
## $cluster.size
## [1] 4 3 2
##
## $min.cluster.size
## [1] 2
##
## $noisen
## [1] 0
##
## $diameter
## [1] 0.5068531 0.5913357 0.1680609
##
## $average.distance
## [1] 0.4002332 0.5152329 0.1680609
##
## $median.distance
## [1] 0.4055768 0.5314759 0.1680609
##
## $separation
## [1] 0.5229192 0.5952870 0.5229192
##
## $average.toother
## [1] 0.8850877 1.1943831 1.1381486
##
## $separation.matrix
## [,1] [,2] [,3]
## [1,] 0.0000000 0.595287 0.5229192
## [2,] 0.5952870 0.000000 1.4083008
## [3,] 0.5229192 1.408301 0.0000000
##
## $ave.between.matrix
## [,1] [,2] [,3]
## [1,] 0.0000000 0.9694404 0.7585586
## [2,] 0.9694404 0.0000000 1.6442685
## [3,] 0.7585586 1.6442685 0.0000000
##
## $average.between
## [1] 1.060283
##
## $average.within
## [1] 0.4115159
##
## $n.between
## [1] 26
##
## $n.within
## [1] 10
##
## $max.diameter
## [1] 0.5913357
##
## $min.separation
## [1] 0.5229192
##
## $within.cluster.ss
## [1] 0.5365621
##
## $clus.avg.silwidths
## 1 2 3
## 0.4281916 0.4560306 0.7772011
##
## $avg.silwidth
## [1] 0.5150289
##
## $g2
## NULL
##
## $g3
## NULL
##
## $pearsongamma
## [1] 0.646884
##
## $dunn
## [1] 0.8843018
##
## $dunn2
## [1] 1.472264
##
## $entropy
## [1] 1.060857
##
## $wb.ratio
## [1] 0.3881188
##
## $ch
## [1] 18.8348
##
## $cwidegap
## [1] 0.3865230 0.5314759 0.1680609
##
## $widestgap
## [1] 0.5314759
##
## $sindex
## [1] 0.5229192
##
## $corrected.rand
## [1] 1
##
## $vi
## [1] 0where d is a distance matrix among objects, and fit1\(cluster and fit\)cluster are integer vectors containing classification results from two different clusterings of the same data.
library(SASmixed)
data(Multilocation)
mixed.res <- standard.sensitivity.plot(Multilocation,
response = "Adj",
TreatmentName = "Trt",
TrialName = "Location",
RepName="Block",
dual.dendrogram=TRUE,
plot.outliers=TRUE,legend.columns=1)## Loading required package: lme4
## Loading required package: Matrix## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?
print.stdplot(mixed.res)## [1] AOV
## [1] ----------------------------------------------------
## Analysis of Variance Table
##
## Response: Adj
## Df Sum Sq Mean Sq F value Pr(>F)
## Location 8 11.4635 1.43294 41.4400 < 2.2e-16 ***
## Trt 3 1.2217 0.40725 11.7774 4.803e-06 ***
## Location:Trt 24 0.9966 0.04152 1.2008 0.28285
## Location:Block 18 1.0270 0.05706 1.6500 0.07994 .
## Residuals 54 1.8672 0.03458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] Mixed Model
## [1] ----------------------------------------------------
## Linear mixed model fit by REML ['lmerMod']
## Formula: Adj ~ Location + (1 | Location/Block) + (1 | Location:Trt)
## Data: plot.dat
##
## REML criterion at convergence: 2.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6168 -0.6321 0.0162 0.5230 2.8392
##
## Random effects:
## Groups Name Variance Std.Dev.
## Location:Trt (Intercept) 0.015860 0.12594
## Block:Location (Intercept) 0.005619 0.07496
## Location (Intercept) 0.028356 0.16839
## Residual 0.034579 0.18595
## Number of obs: 108, groups:
## Location:Trt, 36; Block:Location, 27; Location, 9
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.99968 0.19255 15.579
## LocationB -0.70262 0.27231 -2.580
## LocationC 0.04893 0.27231 0.180
## LocationD -0.44917 0.27231 -1.649
## LocationE -0.15282 0.27231 -0.561
## LocationF 0.25891 0.27231 0.951
## LocationG -0.15720 0.27231 -0.577
## LocationH 0.32281 0.27231 1.185
## LocationI -0.47818 0.27231 -1.756
##
## Correlation of Fixed Effects:
## (Intr) LoctnB LoctnC LoctnD LoctnE LoctnF LoctnG LoctnH
## LocationB -0.707
## LocationC -0.707 0.500
## LocationD -0.707 0.500 0.500
## LocationE -0.707 0.500 0.500 0.500
## LocationF -0.707 0.500 0.500 0.500 0.500
## LocationG -0.707 0.500 0.500 0.500 0.500 0.500
## LocationH -0.707 0.500 0.500 0.500 0.500 0.500 0.500
## LocationI -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## convergence code: 0
## Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?
##
## [1]
## [1] Stability
## [1] ----------------------------------------------------
## Treatment Slope Intercept Mean SD b
## 1 1 1.0681951 -0.12482441 2.924011 0.3852070 0.068195097
## 2 2 0.9601752 -0.06288138 2.677644 0.3403034 -0.039824803
## 3 3 1.0084981 0.07100300 2.949452 0.3584278 0.008498139
## 4 4 0.9631316 0.11670279 2.865667 0.3556809 -0.036868433
## Pb bR2
## 1 0.5890136 0.043775237
## 2 0.6447955 0.032068736
## 3 0.9287174 0.001226858
## 4 0.7959259 0.010207069
## [1]
## [1] Tukey's 1 d.f.
## [1] ----------------------------------------------------
##
## Call:
## lm(formula = as.formula(modelString), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.37605 -0.12428 -0.01798 0.11932 0.43786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.245e+00 1.015e-01 31.974 < 2e-16 ***
## Trt2 -2.464e-01 5.240e-02 -4.702 1.11e-05 ***
## Trt3 2.544e-02 5.240e-02 0.486 0.628700
## Trt4 -5.834e-02 5.240e-02 -1.113 0.268991
## LocationB -9.411e-01 1.361e-01 -6.912 1.22e-09 ***
## LocationC -5.245e-02 1.361e-01 -0.385 0.701107
## LocationD -7.574e-01 1.361e-01 -5.563 3.69e-07 ***
## LocationE -4.590e-01 1.361e-01 -3.371 0.001172 **
## LocationF -2.723e-02 1.361e-01 -0.200 0.842027
## LocationG -2.003e-01 1.361e-01 -1.471 0.145247
## LocationH 1.762e-01 1.361e-01 1.294 0.199515
## LocationI -4.800e-01 1.361e-01 -3.526 0.000715 ***
## LocationA:Block2 -2.216e-01 1.361e-01 -1.628 0.107671
## LocationB:Block2 1.214e-01 1.361e-01 0.892 0.375421
## LocationC:Block2 -1.820e-01 1.361e-01 -1.337 0.185092
## LocationD:Block2 2.832e-01 1.361e-01 2.080 0.040818 *
## LocationE:Block2 1.871e-01 1.361e-01 1.374 0.173392
## LocationF:Block2 9.575e-02 1.361e-01 0.703 0.483984
## LocationG:Block2 -2.384e-01 1.361e-01 -1.751 0.083845 .
## LocationH:Block2 -8.525e-02 1.361e-01 -0.626 0.533046
## LocationI:Block2 -3.204e-01 1.361e-01 -2.353 0.021173 *
## LocationA:Block3 -3.036e-01 1.361e-01 -2.230 0.028684 *
## LocationB:Block3 6.885e-02 1.361e-01 0.506 0.614496
## LocationC:Block3 -3.895e-02 1.361e-01 -0.286 0.775571
## LocationD:Block3 1.162e-01 1.361e-01 0.853 0.396119
## LocationE:Block3 2.062e-01 1.361e-01 1.515 0.133876
## LocationF:Block3 2.375e-01 1.361e-01 1.745 0.085062 .
## LocationG:Block3 -1.573e-01 1.361e-01 -1.156 0.251420
## LocationH:Block3 4.854e-18 1.361e-01 0.000 1.000000
## LocationI:Block3 -1.993e-01 1.361e-01 -1.464 0.147188
## eTrt:eLocation 2.473e-01 4.894e-01 0.505 0.614745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1925 on 77 degrees of freedom
## Multiple R-squared: 0.8278, Adjusted R-squared: 0.7607
## F-statistic: 12.34 on 30 and 77 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 3 1.222 0.4072 10.986 4.39e-06 ***
## Location 8 11.464 1.4329 38.656 < 2e-16 ***
## Location:Block 18 1.027 0.0571 1.539 0.0995 .
## eTrt:eLocation 1 0.009 0.0095 0.255 0.6147
## Residuals 77 2.854 0.0371
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1]
## [1] Heterogenous Slopes
## [1] ----------------------------------------------------
##
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33457 -0.12065 -0.02561 0.12390 0.40986
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.207e+00 1.049e-01 30.573 < 2e-16 ***
## Trt2 -2.464e-01 5.229e-02 -4.712 1.11e-05 ***
## Trt3 2.544e-02 5.229e-02 0.487 0.62799
## Trt4 -5.834e-02 5.229e-02 -1.116 0.26805
## LocationB -8.252e-01 1.601e-01 -5.155 1.99e-06 ***
## LocationC -4.599e-02 1.359e-01 -0.338 0.73606
## LocationD -6.641e-01 1.520e-01 -4.370 3.94e-05 ***
## LocationE -4.024e-01 1.420e-01 -2.834 0.00590 **
## LocationF -2.387e-02 1.359e-01 -0.176 0.86101
## LocationG -1.756e-01 1.370e-01 -1.282 0.20387
## LocationH 1.545e-01 1.368e-01 1.129 0.26230
## LocationI -4.209e-01 1.425e-01 -2.953 0.00421 **
## LocationA:Block2 -2.216e-01 1.358e-01 -1.631 0.10703
## LocationB:Block2 1.214e-01 1.358e-01 0.893 0.37446
## LocationC:Block2 -1.820e-01 1.358e-01 -1.340 0.18425
## LocationD:Block2 2.832e-01 1.358e-01 2.085 0.04048 *
## LocationE:Block2 1.871e-01 1.358e-01 1.377 0.17257
## LocationF:Block2 9.575e-02 1.358e-01 0.705 0.48309
## LocationG:Block2 -2.384e-01 1.358e-01 -1.755 0.08329 .
## LocationH:Block2 -8.525e-02 1.358e-01 -0.628 0.53221
## LocationI:Block2 -3.204e-01 1.358e-01 -2.358 0.02097 *
## LocationA:Block3 -3.036e-01 1.358e-01 -2.235 0.02843 *
## LocationB:Block3 6.885e-02 1.358e-01 0.507 0.61377
## LocationC:Block3 -3.895e-02 1.358e-01 -0.287 0.77512
## LocationD:Block3 1.162e-01 1.358e-01 0.855 0.39517
## LocationE:Block3 2.062e-01 1.358e-01 1.518 0.13315
## LocationF:Block3 2.375e-01 1.358e-01 1.748 0.08450 .
## LocationG:Block3 -1.573e-01 1.358e-01 -1.158 0.25049
## LocationH:Block3 4.416e-18 1.358e-01 0.000 1.00000
## LocationI:Block3 -1.993e-01 1.358e-01 -1.467 0.14643
## Trt1:eLocation 2.321e-01 1.469e-01 1.580 0.11840
## Trt2:eLocation 1.098e-01 1.469e-01 0.747 0.45725
## Trt3:eLocation 1.509e-01 1.469e-01 1.027 0.30776
## Trt4:eLocation NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1921 on 75 degrees of freedom
## Multiple R-squared: 0.833, Adjusted R-squared: 0.7618
## F-statistic: 11.69 on 32 and 75 DF, p-value: < 2.2e-16
##
##
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.53048 -0.13666 0.00839 0.13721 0.53582
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## Trt1 2.92401 0.04358 67.092 < 2e-16 ***
## Trt2 2.67764 0.04358 61.439 < 2e-16 ***
## Trt3 2.94945 0.04358 67.676 < 2e-16 ***
## Trt4 2.86567 0.04358 65.753 < 2e-16 ***
## Trt1:eLocation 0.96942 0.12246 7.917 3.43e-12 ***
## Trt2:eLocation 0.84714 0.12246 6.918 4.42e-10 ***
## Trt3:eLocation 0.88822 0.12246 7.253 8.81e-11 ***
## Trt4:eLocation 0.73736 0.12246 6.021 2.88e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2265 on 100 degrees of freedom
## Multiple R-squared: 0.9943, Adjusted R-squared: 0.9938
## F-statistic: 2172 on 8 and 100 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 4 881.0 220.26 4294.92 <2e-16 ***
## Trt:eLocation 4 10.2 2.56 49.85 <2e-16 ***
## Residuals 100 5.1 0.05
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 3 1.222 0.4072 11.034 4.42e-06 ***
## Location 8 11.464 1.4329 38.824 < 2e-16 ***
## Location:Block 18 1.027 0.0571 1.546 0.0981 .
## Trt:eLocation 3 0.096 0.0319 0.864 0.4636
## Residuals 75 2.768 0.0369
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1]
## [1] Random Outliers
## [1] ----------------------------------------------------
## [1] Critical Value:
## [1] 0.5578604
## [1] Interaction sd Value:
## [1] 0.1201781
## [1] Error sd Value:
## [1] 0.1859535
## [1] Pairs:
## list()
## [1]
## [1]response = "Plot.Mean"
TreatmentName = "Criteria.Entry.No."
TrialName = "Expt.No."
RepName="Rep.No."
#yield.desc ="Yield"
#tw.desc="Test Weight"
#hd.desc="Heading"
#ht.desc="Test Weight"
#cpt.dat <- read.csv("CPT.full.subset.csv",header=TRUE)
#cpt.dat <- read.csv("CPT_2007Subsetb.csv",header=TRUE)
yield.desc ="GY"
tw.desc ="TW"
hd.desc="HD"
ht.desc="HT"
cpt.dat <- read.delim("CPT_2007Subset.txt",header=TRUE)
cpt.dat <- subset(cpt.dat,!is.na(cpt.dat$Plot.Mean))
cpt.dat$Expt.No. <- as.factor(cpt.dat$Expt.No.)
TrtNames <- as.character(cpt.dat$Criteria.Entry.No.)
TrtNames[cpt.dat$Criteria.Entry.No.<10] <- paste("0",TrtNames[cpt.dat$Criteria.Entry.No.<10],sep="")
cpt.dat$Criteria.Entry.No. <- as.factor(TrtNames)
cpt.dat$Rep.No. <- as.factor(cpt.dat$Rep.No.)
gy.dat <- subset(cpt.dat,cpt.dat$Description==yield.desc)
tw.dat <- subset(cpt.dat,cpt.dat$Description==tw.desc)
hd.dat <- subset(cpt.dat,cpt.dat$Description==hd.desc)
ht.dat <- subset(cpt.dat,cpt.dat$Description==ht.desc)
gy.dat$Expt.No. <- as.factor(as.character(gy.dat$Expt.No.))
tw.dat$Expt.No. <- as.factor(as.character(tw.dat$Expt.No.))
hd.dat$Expt.No. <- as.factor(as.character(hd.dat$Expt.No.))
ht.dat$Expt.No. <- as.factor(as.character(ht.dat$Expt.No.))gy.means <- gei.table.and.effects(gy.dat,
response = response,
TreatmentName = TreatmentName,
TrialName = TrialName,
RepName=RepName)
gy.means$trial.means## [,1]
## 1 53.67708
## 2 55.57483
## 3 57.78769
## 4 39.01086
## 5 39.54350
## 6 33.59267
## 7 63.30659
## 8 67.83786
## 9 62.65739
## 10 26.95339
## 11 41.62739
## 12 39.34495
## 13 45.65942colMeans(gy.means$means.table)## [1] 53.67708 45.76792 56.31240 52.48333 44.91626 34.96458 55.53854
## [8] 58.18740 54.31667 30.64583 47.12352 48.92969 43.71042gy.res <- standard.sensitivity.plot(gy.dat,
response = response,
TreatmentName = TreatmentName,
TrialName = TrialName,
RepName=RepName,
dual.dendrogram=FALSE,
plot.outliers=TRUE,legend.columns=3)
gy.res <- standard.sensitivity.plot(gy.dat,
response = response,
TreatmentName = TreatmentName,
TrialName = TrialName,
RepName=RepName,
outliers=2.4,
dual.dendrogram=TRUE,
plot.outliers=TRUE,legend.columns=3)
print.stdplot(gy.res)## [1] AOV
## [1] ----------------------------------------------------
## Analysis of Variance Table
##
## Response: Plot.Mean
## Df Sum Sq Mean Sq F value Pr(>F)
## Expt.No. 12 39698 3308.2 194.3594 < 2.2e-16 ***
## Criteria.Entry.No. 11 17509 1591.7 93.5136 < 2.2e-16 ***
## Expt.No.:Criteria.Entry.No. 132 19964 151.2 8.8856 < 2.2e-16 ***
## Expt.No.:Rep.No. 39 3998 102.5 6.0232 < 2.2e-16 ***
## Residuals 429 7302 17.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] Mixed Model
## [1] ----------------------------------------------------
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## Plot.Mean ~ Expt.No. + (1 | Expt.No./Rep.No.) + (1 | Expt.No.:Criteria.Entry.No.)
## Data: plot.dat
##
## REML criterion at convergence: 3977.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.95766 -0.49670 -0.00515 0.49975 2.82744
##
## Random effects:
## Groups Name Variance Std.Dev.
## Expt.No.:Criteria.Entry.No. (Intercept) 61.256 7.827
## Rep.No.:Expt.No. (Intercept) 7.125 2.669
## Expt.No. (Intercept) 32.377 5.690
## Residual 17.021 4.126
## Number of obs: 624, groups:
## Expt.No.:Criteria.Entry.No., 156; Rep.No.:Expt.No., 52; Expt.No., 13
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 53.6771 6.2942 8.528
## Expt.No.CPT:2007:Brookings -7.9092 8.9014 -0.889
## Expt.No.CPT:2007:DLakesPea 2.6353 8.9014 0.296
## Expt.No.CPT:2007:Hayes -1.1937 8.9014 -0.134
## Expt.No.CPT:2007:Kennebec -8.7608 8.9014 -0.984
## Expt.No.CPT:2007:Martin -18.7125 8.9014 -2.102
## Expt.No.CPT:2007:Onida 1.8615 8.9014 0.209
## Expt.No.CPT:2007:Platte 4.5103 8.9014 0.507
## Expt.No.CPT:2007:Selby 0.6396 8.9014 0.072
## Expt.No.CPT:2007:Sturgis -23.0312 8.9014 -2.587
## Expt.No.CPT:2007:Wall -6.5536 8.9014 -0.736
## Expt.No.CPT:2007:Watertown -4.7474 8.9014 -0.533
## Expt.No.CPT:2007:Winner -9.9667 8.9014 -1.120
##
## Correlation of Fixed Effects:
## (Intr) E.N.CPT:2007:B E.N.CPT:2007:D E.N.CPT:2007:H
## E.N.CPT:2007:B -0.707
## E.N.CPT:2007:D -0.707 0.500
## E.N.CPT:2007:H -0.707 0.500 0.500
## E.N.CPT:2007:K -0.707 0.500 0.500 0.500
## E.N.CPT:2007:M -0.707 0.500 0.500 0.500
## E.N.CPT:2007:O -0.707 0.500 0.500 0.500
## E.N.CPT:2007:P -0.707 0.500 0.500 0.500
## Expt.N.CPT:2007:Sl -0.707 0.500 0.500 0.500
## Expt.N.CPT:2007:St -0.707 0.500 0.500 0.500
## Expt.N.CPT:2007:Wl -0.707 0.500 0.500 0.500
## Expt.N.CPT:2007:Wt -0.707 0.500 0.500 0.500
## Expt.N.CPT:2007:Wn -0.707 0.500 0.500 0.500
## E.N.CPT:2007:K E.N.CPT:2007:M E.N.CPT:2007:O
## E.N.CPT:2007:B
## E.N.CPT:2007:D
## E.N.CPT:2007:H
## E.N.CPT:2007:K
## E.N.CPT:2007:M 0.500
## E.N.CPT:2007:O 0.500 0.500
## E.N.CPT:2007:P 0.500 0.500 0.500
## Expt.N.CPT:2007:Sl 0.500 0.500 0.500
## Expt.N.CPT:2007:St 0.500 0.500 0.500
## Expt.N.CPT:2007:Wl 0.500 0.500 0.500
## Expt.N.CPT:2007:Wt 0.500 0.500 0.500
## Expt.N.CPT:2007:Wn 0.500 0.500 0.500
## E.N.CPT:2007:P Expt.N.CPT:2007:Sl Expt.N.CPT:2007:St
## E.N.CPT:2007:B
## E.N.CPT:2007:D
## E.N.CPT:2007:H
## E.N.CPT:2007:K
## E.N.CPT:2007:M
## E.N.CPT:2007:O
## E.N.CPT:2007:P
## Expt.N.CPT:2007:Sl 0.500
## Expt.N.CPT:2007:St 0.500 0.500
## Expt.N.CPT:2007:Wl 0.500 0.500 0.500
## Expt.N.CPT:2007:Wt 0.500 0.500 0.500
## Expt.N.CPT:2007:Wn 0.500 0.500 0.500
## Expt.N.CPT:2007:Wl Expt.N.CPT:2007:Wt
## E.N.CPT:2007:B
## E.N.CPT:2007:D
## E.N.CPT:2007:H
## E.N.CPT:2007:K
## E.N.CPT:2007:M
## E.N.CPT:2007:O
## E.N.CPT:2007:P
## Expt.N.CPT:2007:Sl
## Expt.N.CPT:2007:St
## Expt.N.CPT:2007:Wl
## Expt.N.CPT:2007:Wt 0.500
## Expt.N.CPT:2007:Wn 0.500 0.500
## [1]
## [1] Stability
## [1] ----------------------------------------------------
## Treatment Slope Intercept Mean SD b
## 1 1 1.4470667 -19.3059998 50.43968 13.881179 0.447066711
## 2 2 1.1321911 -6.2009466 48.36837 10.210409 0.132191105
## 3 3 0.6213510 15.2685346 45.21639 8.308268 -0.378649035
## 4 4 0.5836777 7.1322629 35.26434 9.429917 -0.416322288
## 5 5 1.2073912 -3.8692090 54.32460 12.212059 0.207391158
## 6 6 1.2629921 -4.0818885 56.79177 12.518736 0.262992078
## 7 7 0.9908940 -1.5992298 46.15985 9.914119 -0.009105978
## 8 8 0.7606544 6.5102431 43.17224 7.677330 -0.239345577
## 9 9 0.9801060 0.4427417 47.68186 8.695932 -0.019894018
## 10 10 1.0053054 2.5710136 51.02470 8.950710 0.005305443
## 11 11 0.9178223 5.4363092 49.67348 8.668054 -0.082177704
## 12 12 1.0905481 -2.3038313 50.25838 9.701141 0.090548106
## Pb bR2
## 1 0.1044094 0.2216659526
## 2 0.3810162 0.0703845599
## 3 0.1377338 0.1889368944
## 4 0.1841813 0.1543568700
## 5 0.4304031 0.0574180092
## 6 0.3124248 0.0924748910
## 7 0.9646718 0.0001866071
## 8 0.1593920 0.1715657615
## 9 0.8615537 0.0028891132
## 10 0.9647863 0.0001853985
## 11 0.5949401 0.0265318385
## 12 0.4892785 0.0444565619
## [1]
## [1] Tukey's 1 d.f.
## [1] ----------------------------------------------------
##
## Call:
## lm(formula = as.formula(modelString), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.2628 -3.9778 0.1832 3.8793 20.6651
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.408376 2.169661 26.460 < 2e-16
## Criteria.Entry.No.02 -2.071312 1.339144 -1.547 0.122490
## Criteria.Entry.No.03 -5.223290 1.339144 -3.900 0.000108
## Criteria.Entry.No.04 -15.175336 1.339144 -11.332 < 2e-16
## Criteria.Entry.No.05 3.884915 1.339144 2.901 0.003865
## Criteria.Entry.No.06 6.352087 1.339144 4.743 2.67e-06
## Criteria.Entry.No.07 -4.279829 1.339144 -3.196 0.001472
## Criteria.Entry.No.08 -7.267437 1.339144 -5.427 8.55e-08
## Criteria.Entry.No.09 -2.757819 1.339144 -2.059 0.039919
## Criteria.Entry.No.10 0.585016 1.339144 0.437 0.662382
## Criteria.Entry.No.11 -0.766198 1.339144 -0.572 0.567446
## Criteria.Entry.No.12 -0.181305 1.339144 -0.135 0.892353
## Expt.No.CPT:2007:Brookings -7.235833 2.787650 -2.596 0.009688
## Expt.No.CPT:2007:DLakesPea 2.829582 2.787650 1.015 0.310524
## Expt.No.CPT:2007:Hayes -7.083334 2.787650 -2.541 0.011323
## Expt.No.CPT:2007:Kennebec -11.287547 2.787650 -4.049 5.87e-05
## Expt.No.CPT:2007:Martin -21.766667 2.787650 -7.808 2.87e-14
## Expt.No.CPT:2007:Onida -1.084167 2.787650 -0.389 0.697485
## Expt.No.CPT:2007:Platte 4.754999 2.787650 1.706 0.088611
## Expt.No.CPT:2007:Selby -2.528334 2.787650 -0.907 0.364809
## Expt.No.CPT:2007:Sturgis -24.325000 2.787650 -8.726 < 2e-16
## Expt.No.CPT:2007:Wall -6.553920 2.787650 -2.351 0.019066
## Expt.No.CPT:2007:Watertown -10.402501 2.787650 -3.732 0.000210
## Expt.No.CPT:2007:Winner -12.813751 2.787650 -4.597 5.31e-06
## Expt.No.CPT:2007:Bison:Rep.No.2 -2.725000 2.787650 -0.978 0.328731
## Expt.No.CPT:2007:Brookings:Rep.No.2 -4.190833 2.787650 -1.503 0.133311
## Expt.No.CPT:2007:DLakesPea:Rep.No.2 0.459168 2.787650 0.165 0.869228
## Expt.No.CPT:2007:Hayes:Rep.No.2 1.408333 2.787650 0.505 0.613614
## Expt.No.CPT:2007:Kennebec:Rep.No.2 2.631682 2.787650 0.944 0.345551
## Expt.No.CPT:2007:Martin:Rep.No.2 1.308333 2.787650 0.469 0.639015
## Expt.No.CPT:2007:Onida:Rep.No.2 2.271667 2.787650 0.815 0.415474
## Expt.No.CPT:2007:Platte:Rep.No.2 -1.957917 2.787650 -0.702 0.482750
## Expt.No.CPT:2007:Selby:Rep.No.2 2.442500 2.787650 0.876 0.381305
## Expt.No.CPT:2007:Sturgis:Rep.No.2 1.550000 2.787650 0.556 0.578417
## Expt.No.CPT:2007:Wall:Rep.No.2 8.108943 2.787650 2.909 0.003771
## Expt.No.CPT:2007:Watertown:Rep.No.2 6.613333 2.787650 2.372 0.018011
## Expt.No.CPT:2007:Winner:Rep.No.2 1.185834 2.787650 0.425 0.670717
## Expt.No.CPT:2007:Bison:Rep.No.3 -1.825001 2.787650 -0.655 0.512947
## Expt.No.CPT:2007:Brookings:Rep.No.3 -3.180000 2.787650 -1.141 0.254463
## Expt.No.CPT:2007:DLakesPea:Rep.No.3 -3.361666 2.787650 -1.206 0.228360
## Expt.No.CPT:2007:Hayes:Rep.No.3 4.975000 2.787650 1.785 0.074858
## Expt.No.CPT:2007:Kennebec:Rep.No.3 0.902049 2.787650 0.324 0.746371
## Expt.No.CPT:2007:Martin:Rep.No.3 3.433334 2.787650 1.232 0.218607
## Expt.No.CPT:2007:Onida:Rep.No.3 0.751668 2.787650 0.270 0.787535
## Expt.No.CPT:2007:Platte:Rep.No.3 -4.097500 2.787650 -1.470 0.142157
## Expt.No.CPT:2007:Selby:Rep.No.3 0.151667 2.787650 0.054 0.956631
## Expt.No.CPT:2007:Sturgis:Rep.No.3 -1.225000 2.787650 -0.439 0.660514
## Expt.No.CPT:2007:Wall:Rep.No.3 -7.933019 2.787650 -2.846 0.004593
## Expt.No.CPT:2007:Watertown:Rep.No.3 3.924584 2.787650 1.408 0.159731
## Expt.No.CPT:2007:Winner:Rep.No.3 2.340417 2.787650 0.840 0.401510
## Expt.No.CPT:2007:Bison:Rep.No.4 -1.408334 2.787650 -0.505 0.613614
## Expt.No.CPT:2007:Brookings:Rep.No.4 -1.280833 2.787650 -0.459 0.646077
## Expt.No.CPT:2007:DLakesPea:Rep.No.4 -3.832916 2.787650 -1.375 0.169693
## Expt.No.CPT:2007:Hayes:Rep.No.4 11.216667 2.787650 4.024 6.52e-05
## Expt.No.CPT:2007:Kennebec:Rep.No.4 0.614842 2.787650 0.221 0.825516
## Expt.No.CPT:2007:Martin:Rep.No.4 1.516667 2.787650 0.544 0.586612
## Expt.No.CPT:2007:Onida:Rep.No.4 2.800834 2.787650 1.005 0.315461
## Expt.No.CPT:2007:Platte:Rep.No.4 -0.881666 2.787650 -0.316 0.751911
## Expt.No.CPT:2007:Selby:Rep.No.4 4.119167 2.787650 1.478 0.140064
## Expt.No.CPT:2007:Sturgis:Rep.No.4 -1.108333 2.787650 -0.398 0.691086
## Expt.No.CPT:2007:Wall:Rep.No.4 -6.132848 2.787650 -2.200 0.028214
## Expt.No.CPT:2007:Watertown:Rep.No.4 6.124167 2.787650 2.197 0.028437
## Expt.No.CPT:2007:Winner:Rep.No.4 1.903751 2.787650 0.683 0.494938
## eCriteria.Entry.No.:eExpt.No. 0.030572 0.006142 4.978 8.58e-07
##
## (Intercept) ***
## Criteria.Entry.No.02
## Criteria.Entry.No.03 ***
## Criteria.Entry.No.04 ***
## Criteria.Entry.No.05 **
## Criteria.Entry.No.06 ***
## Criteria.Entry.No.07 **
## Criteria.Entry.No.08 ***
## Criteria.Entry.No.09 *
## Criteria.Entry.No.10
## Criteria.Entry.No.11
## Criteria.Entry.No.12
## Expt.No.CPT:2007:Brookings **
## Expt.No.CPT:2007:DLakesPea
## Expt.No.CPT:2007:Hayes *
## Expt.No.CPT:2007:Kennebec ***
## Expt.No.CPT:2007:Martin ***
## Expt.No.CPT:2007:Onida
## Expt.No.CPT:2007:Platte .
## Expt.No.CPT:2007:Selby
## Expt.No.CPT:2007:Sturgis ***
## Expt.No.CPT:2007:Wall *
## Expt.No.CPT:2007:Watertown ***
## Expt.No.CPT:2007:Winner ***
## Expt.No.CPT:2007:Bison:Rep.No.2
## Expt.No.CPT:2007:Brookings:Rep.No.2
## Expt.No.CPT:2007:DLakesPea:Rep.No.2
## Expt.No.CPT:2007:Hayes:Rep.No.2
## Expt.No.CPT:2007:Kennebec:Rep.No.2
## Expt.No.CPT:2007:Martin:Rep.No.2
## Expt.No.CPT:2007:Onida:Rep.No.2
## Expt.No.CPT:2007:Platte:Rep.No.2
## Expt.No.CPT:2007:Selby:Rep.No.2
## Expt.No.CPT:2007:Sturgis:Rep.No.2
## Expt.No.CPT:2007:Wall:Rep.No.2 **
## Expt.No.CPT:2007:Watertown:Rep.No.2 *
## Expt.No.CPT:2007:Winner:Rep.No.2
## Expt.No.CPT:2007:Bison:Rep.No.3
## Expt.No.CPT:2007:Brookings:Rep.No.3
## Expt.No.CPT:2007:DLakesPea:Rep.No.3
## Expt.No.CPT:2007:Hayes:Rep.No.3 .
## Expt.No.CPT:2007:Kennebec:Rep.No.3
## Expt.No.CPT:2007:Martin:Rep.No.3
## Expt.No.CPT:2007:Onida:Rep.No.3
## Expt.No.CPT:2007:Platte:Rep.No.3
## Expt.No.CPT:2007:Selby:Rep.No.3
## Expt.No.CPT:2007:Sturgis:Rep.No.3
## Expt.No.CPT:2007:Wall:Rep.No.3 **
## Expt.No.CPT:2007:Watertown:Rep.No.3
## Expt.No.CPT:2007:Winner:Rep.No.3
## Expt.No.CPT:2007:Bison:Rep.No.4
## Expt.No.CPT:2007:Brookings:Rep.No.4
## Expt.No.CPT:2007:DLakesPea:Rep.No.4
## Expt.No.CPT:2007:Hayes:Rep.No.4 ***
## Expt.No.CPT:2007:Kennebec:Rep.No.4
## Expt.No.CPT:2007:Martin:Rep.No.4
## Expt.No.CPT:2007:Onida:Rep.No.4
## Expt.No.CPT:2007:Platte:Rep.No.4
## Expt.No.CPT:2007:Selby:Rep.No.4
## Expt.No.CPT:2007:Sturgis:Rep.No.4
## Expt.No.CPT:2007:Wall:Rep.No.4 *
## Expt.No.CPT:2007:Watertown:Rep.No.4 *
## Expt.No.CPT:2007:Winner:Rep.No.4
## eCriteria.Entry.No.:eExpt.No. ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.828 on 560 degrees of freedom
## Multiple R-squared: 0.7049, Adjusted R-squared: 0.6717
## F-statistic: 21.23 on 63 and 560 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## Criteria.Entry.No. 11 17509 1592 34.137 < 2e-16 ***
## Expt.No. 12 39698 3308 70.951 < 2e-16 ***
## Expt.No.:Rep.No. 39 3998 103 2.199 6.14e-05 ***
## eCriteria.Entry.No.:eExpt.No. 1 1155 1155 24.777 8.58e-07 ***
## Residuals 560 26111 47
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1]
## [1] Heterogenous Slopes
## [1] ----------------------------------------------------
##
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.1268 -3.6831 -0.0066 3.7652 20.5194
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.84208 2.29014 25.257 < 2e-16
## Criteria.Entry.No.02 -2.07131 1.32439 -1.564 0.118398
## Criteria.Entry.No.03 -5.22329 1.32439 -3.944 9.05e-05
## Criteria.Entry.No.04 -15.17534 1.32439 -11.458 < 2e-16
## Criteria.Entry.No.05 3.88492 1.32439 2.933 0.003493
## Criteria.Entry.No.06 6.35209 1.32439 4.796 2.08e-06
## Criteria.Entry.No.07 -4.27983 1.32439 -3.232 0.001305
## Criteria.Entry.No.08 -7.26744 1.32439 -5.487 6.23e-08
## Criteria.Entry.No.09 -2.75782 1.32439 -2.082 0.037773
## Criteria.Entry.No.10 0.58502 1.32439 0.442 0.658861
## Criteria.Entry.No.11 -0.76620 1.32439 -0.579 0.563143
## Criteria.Entry.No.12 -0.18131 1.32439 -0.137 0.891162
## Expt.No.CPT:2007:Brookings -7.65428 2.86302 -2.674 0.007729
## Expt.No.CPT:2007:DLakesPea 2.99322 2.77342 1.079 0.280948
## Expt.No.CPT:2007:Hayes -7.49296 2.85867 -2.621 0.009006
## Expt.No.CPT:2007:Kennebec -11.94030 3.00857 -3.969 8.18e-05
## Expt.No.CPT:2007:Martin -23.02542 3.60497 -6.387 3.60e-10
## Expt.No.CPT:2007:Onida -1.14686 2.75936 -0.416 0.677845
## Expt.No.CPT:2007:Platte 5.02998 2.80324 1.794 0.073307
## Expt.No.CPT:2007:Selby -2.67455 2.77010 -0.966 0.334716
## Expt.No.CPT:2007:Sturgis -25.73170 3.78662 -6.795 2.82e-11
## Expt.No.CPT:2007:Wall -6.93293 2.84426 -2.438 0.015104
## Expt.No.CPT:2007:Watertown -11.00407 2.97202 -3.703 0.000235
## Expt.No.CPT:2007:Winner -13.55476 3.07739 -4.405 1.27e-05
## Expt.No.CPT:2007:Bison:Rep.No.2 -2.72500 2.75693 -0.988 0.323383
## Expt.No.CPT:2007:Brookings:Rep.No.2 -4.19083 2.75693 -1.520 0.129058
## Expt.No.CPT:2007:DLakesPea:Rep.No.2 0.45917 2.75693 0.167 0.867785
## Expt.No.CPT:2007:Hayes:Rep.No.2 1.40833 2.75693 0.511 0.609672
## Expt.No.CPT:2007:Kennebec:Rep.No.2 2.63168 2.75693 0.955 0.340215
## Expt.No.CPT:2007:Martin:Rep.No.2 1.30833 2.75693 0.475 0.635288
## Expt.No.CPT:2007:Onida:Rep.No.2 2.27167 2.75693 0.824 0.410306
## Expt.No.CPT:2007:Platte:Rep.No.2 -1.95792 2.75693 -0.710 0.477893
## Expt.No.CPT:2007:Selby:Rep.No.2 2.44250 2.75693 0.886 0.376032
## Expt.No.CPT:2007:Sturgis:Rep.No.2 1.55000 2.75693 0.562 0.574196
## Expt.No.CPT:2007:Wall:Rep.No.2 8.10894 2.75693 2.941 0.003406
## Expt.No.CPT:2007:Watertown:Rep.No.2 6.61333 2.75693 2.399 0.016781
## Expt.No.CPT:2007:Winner:Rep.No.2 1.18583 2.75693 0.430 0.667271
## Expt.No.CPT:2007:Bison:Rep.No.3 -1.82500 2.75693 -0.662 0.508269
## Expt.No.CPT:2007:Brookings:Rep.No.3 -3.18000 2.75693 -1.153 0.249224
## Expt.No.CPT:2007:DLakesPea:Rep.No.3 -3.36167 2.75693 -1.219 0.223234
## Expt.No.CPT:2007:Hayes:Rep.No.3 4.97500 2.75693 1.805 0.071693
## Expt.No.CPT:2007:Kennebec:Rep.No.3 0.90205 2.75693 0.327 0.743646
## Expt.No.CPT:2007:Martin:Rep.No.3 3.43333 2.75693 1.245 0.213535
## Expt.No.CPT:2007:Onida:Rep.No.3 0.75167 2.75693 0.273 0.785227
## Expt.No.CPT:2007:Platte:Rep.No.3 -4.09750 2.75693 -1.486 0.137785
## Expt.No.CPT:2007:Selby:Rep.No.3 0.15167 2.75693 0.055 0.956148
## Expt.No.CPT:2007:Sturgis:Rep.No.3 -1.22500 2.75693 -0.444 0.656975
## Expt.No.CPT:2007:Wall:Rep.No.3 -7.93302 2.75693 -2.877 0.004164
## Expt.No.CPT:2007:Watertown:Rep.No.3 3.92458 2.75693 1.424 0.155148
## Expt.No.CPT:2007:Winner:Rep.No.3 2.34042 2.75693 0.849 0.396294
## Expt.No.CPT:2007:Bison:Rep.No.4 -1.40833 2.75693 -0.511 0.609672
## Expt.No.CPT:2007:Brookings:Rep.No.4 -1.28083 2.75693 -0.465 0.642411
## Expt.No.CPT:2007:DLakesPea:Rep.No.4 -3.83292 2.75693 -1.390 0.165005
## Expt.No.CPT:2007:Hayes:Rep.No.4 11.21667 2.75693 4.069 5.42e-05
## Expt.No.CPT:2007:Kennebec:Rep.No.4 0.61484 2.75693 0.223 0.823605
## Expt.No.CPT:2007:Martin:Rep.No.4 1.51667 2.75693 0.550 0.582454
## Expt.No.CPT:2007:Onida:Rep.No.4 2.80083 2.75693 1.016 0.310112
## Expt.No.CPT:2007:Platte:Rep.No.4 -0.88167 2.75693 -0.320 0.749242
## Expt.No.CPT:2007:Selby:Rep.No.4 4.11917 2.75693 1.494 0.135719
## Expt.No.CPT:2007:Sturgis:Rep.No.4 -1.10833 2.75693 -0.402 0.687828
## Expt.No.CPT:2007:Wall:Rep.No.4 -6.13285 2.75693 -2.225 0.026518
## Expt.No.CPT:2007:Watertown:Rep.No.4 6.12417 2.75693 2.221 0.026732
## Expt.No.CPT:2007:Winner:Rep.No.4 1.90375 2.75693 0.691 0.490151
## Criteria.Entry.No.01:eExpt.No. 0.40283 0.15762 2.556 0.010866
## Criteria.Entry.No.02:eExpt.No. 0.06411 0.15762 0.407 0.684367
## Criteria.Entry.No.03:eExpt.No. -0.37748 0.15762 -2.395 0.016962
## Criteria.Entry.No.04:eExpt.No. -0.40666 0.15762 -2.580 0.010139
## Criteria.Entry.No.05:eExpt.No. 0.08910 0.15762 0.565 0.572138
## Criteria.Entry.No.06:eExpt.No. 0.15470 0.15762 0.981 0.326796
## Criteria.Entry.No.07:eExpt.No. 0.01611 0.15762 0.102 0.918631
## Criteria.Entry.No.08:eExpt.No. -0.29461 0.15762 -1.869 0.062142
## Criteria.Entry.No.09:eExpt.No. -0.15624 0.15762 -0.991 0.322021
## Criteria.Entry.No.10:eExpt.No. -0.09992 0.15762 -0.634 0.526404
## Criteria.Entry.No.11:eExpt.No. -0.08590 0.15762 -0.545 0.585995
## Criteria.Entry.No.12:eExpt.No. NA NA NA NA
##
## (Intercept) ***
## Criteria.Entry.No.02
## Criteria.Entry.No.03 ***
## Criteria.Entry.No.04 ***
## Criteria.Entry.No.05 **
## Criteria.Entry.No.06 ***
## Criteria.Entry.No.07 **
## Criteria.Entry.No.08 ***
## Criteria.Entry.No.09 *
## Criteria.Entry.No.10
## Criteria.Entry.No.11
## Criteria.Entry.No.12
## Expt.No.CPT:2007:Brookings **
## Expt.No.CPT:2007:DLakesPea
## Expt.No.CPT:2007:Hayes **
## Expt.No.CPT:2007:Kennebec ***
## Expt.No.CPT:2007:Martin ***
## Expt.No.CPT:2007:Onida
## Expt.No.CPT:2007:Platte .
## Expt.No.CPT:2007:Selby
## Expt.No.CPT:2007:Sturgis ***
## Expt.No.CPT:2007:Wall *
## Expt.No.CPT:2007:Watertown ***
## Expt.No.CPT:2007:Winner ***
## Expt.No.CPT:2007:Bison:Rep.No.2
## Expt.No.CPT:2007:Brookings:Rep.No.2
## Expt.No.CPT:2007:DLakesPea:Rep.No.2
## Expt.No.CPT:2007:Hayes:Rep.No.2
## Expt.No.CPT:2007:Kennebec:Rep.No.2
## Expt.No.CPT:2007:Martin:Rep.No.2
## Expt.No.CPT:2007:Onida:Rep.No.2
## Expt.No.CPT:2007:Platte:Rep.No.2
## Expt.No.CPT:2007:Selby:Rep.No.2
## Expt.No.CPT:2007:Sturgis:Rep.No.2
## Expt.No.CPT:2007:Wall:Rep.No.2 **
## Expt.No.CPT:2007:Watertown:Rep.No.2 *
## Expt.No.CPT:2007:Winner:Rep.No.2
## Expt.No.CPT:2007:Bison:Rep.No.3
## Expt.No.CPT:2007:Brookings:Rep.No.3
## Expt.No.CPT:2007:DLakesPea:Rep.No.3
## Expt.No.CPT:2007:Hayes:Rep.No.3 .
## Expt.No.CPT:2007:Kennebec:Rep.No.3
## Expt.No.CPT:2007:Martin:Rep.No.3
## Expt.No.CPT:2007:Onida:Rep.No.3
## Expt.No.CPT:2007:Platte:Rep.No.3
## Expt.No.CPT:2007:Selby:Rep.No.3
## Expt.No.CPT:2007:Sturgis:Rep.No.3
## Expt.No.CPT:2007:Wall:Rep.No.3 **
## Expt.No.CPT:2007:Watertown:Rep.No.3
## Expt.No.CPT:2007:Winner:Rep.No.3
## Expt.No.CPT:2007:Bison:Rep.No.4
## Expt.No.CPT:2007:Brookings:Rep.No.4
## Expt.No.CPT:2007:DLakesPea:Rep.No.4
## Expt.No.CPT:2007:Hayes:Rep.No.4 ***
## Expt.No.CPT:2007:Kennebec:Rep.No.4
## Expt.No.CPT:2007:Martin:Rep.No.4
## Expt.No.CPT:2007:Onida:Rep.No.4
## Expt.No.CPT:2007:Platte:Rep.No.4
## Expt.No.CPT:2007:Selby:Rep.No.4
## Expt.No.CPT:2007:Sturgis:Rep.No.4
## Expt.No.CPT:2007:Wall:Rep.No.4 *
## Expt.No.CPT:2007:Watertown:Rep.No.4 *
## Expt.No.CPT:2007:Winner:Rep.No.4
## Criteria.Entry.No.01:eExpt.No. *
## Criteria.Entry.No.02:eExpt.No.
## Criteria.Entry.No.03:eExpt.No. *
## Criteria.Entry.No.04:eExpt.No. *
## Criteria.Entry.No.05:eExpt.No.
## Criteria.Entry.No.06:eExpt.No.
## Criteria.Entry.No.07:eExpt.No.
## Criteria.Entry.No.08:eExpt.No. .
## Criteria.Entry.No.09:eExpt.No.
## Criteria.Entry.No.10:eExpt.No.
## Criteria.Entry.No.11:eExpt.No.
## Criteria.Entry.No.12:eExpt.No.
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.753 on 550 degrees of freedom
## Multiple R-squared: 0.7165, Adjusted R-squared: 0.6789
## F-statistic: 19.04 on 73 and 550 DF, p-value: < 2.2e-16
##
##
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.5936 -4.3246 -0.1103 3.8202 26.2489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## Criteria.Entry.No.01 50.4397 1.0062 50.127 < 2e-16 ***
## Criteria.Entry.No.02 48.3684 1.0062 48.069 < 2e-16 ***
## Criteria.Entry.No.03 45.2164 1.0062 44.936 < 2e-16 ***
## Criteria.Entry.No.04 35.2643 1.0062 35.046 < 2e-16 ***
## Criteria.Entry.No.05 54.3246 1.0062 53.988 < 2e-16 ***
## Criteria.Entry.No.06 56.7918 1.0062 56.440 < 2e-16 ***
## Criteria.Entry.No.07 46.1599 1.0062 45.874 < 2e-16 ***
## Criteria.Entry.No.08 43.1722 1.0062 42.905 < 2e-16 ***
## Criteria.Entry.No.09 47.6819 1.0062 47.386 < 2e-16 ***
## Criteria.Entry.No.10 51.0247 1.0062 50.708 < 2e-16 ***
## Criteria.Entry.No.11 49.6735 1.0062 49.366 < 2e-16 ***
## Criteria.Entry.No.12 50.2584 1.0062 49.947 < 2e-16 ***
## Criteria.Entry.No.01:eExpt.No. 1.3794 0.1198 11.519 < 2e-16 ***
## Criteria.Entry.No.02:eExpt.No. 1.0407 0.1198 8.690 < 2e-16 ***
## Criteria.Entry.No.03:eExpt.No. 0.5991 0.1198 5.003 7.43e-07 ***
## Criteria.Entry.No.04:eExpt.No. 0.5700 0.1198 4.759 2.44e-06 ***
## Criteria.Entry.No.05:eExpt.No. 1.0657 0.1198 8.899 < 2e-16 ***
## Criteria.Entry.No.06:eExpt.No. 1.1313 0.1198 9.447 < 2e-16 ***
## Criteria.Entry.No.07:eExpt.No. 0.9927 0.1198 8.289 7.48e-16 ***
## Criteria.Entry.No.08:eExpt.No. 0.6820 0.1198 5.695 1.93e-08 ***
## Criteria.Entry.No.09:eExpt.No. 0.8204 0.1198 6.850 1.83e-11 ***
## Criteria.Entry.No.10:eExpt.No. 0.8767 0.1198 7.321 7.97e-13 ***
## Criteria.Entry.No.11:eExpt.No. 0.8907 0.1198 7.438 3.56e-13 ***
## Criteria.Entry.No.12:eExpt.No. 0.9766 0.1198 8.155 2.05e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.256 on 600 degrees of freedom
## Multiple R-squared: 0.9795, Adjusted R-squared: 0.9786
## F-statistic: 1192 on 24 and 600 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## Criteria.Entry.No. 12 1467088 122257 2322.05 <2e-16 ***
## Criteria.Entry.No.:eExpt.No. 12 39372 3281 62.32 <2e-16 ***
## Residuals 600 31590 53
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Criteria.Entry.No. 11 17509 1592 34.902 < 2e-16 ***
## Expt.No. 12 39698 3308 72.541 < 2e-16 ***
## Expt.No.:Rep.No. 39 3998 103 2.248 3.82e-05 ***
## Criteria.Entry.No.:eExpt.No. 11 2184 199 4.353 2.88e-06 ***
## Residuals 550 25082 46
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1]
## [1] Random Outliers
## [1] ----------------------------------------------------
## [1] Critical Value:
## [1] 9.901533
## [1] Interaction sd Value:
## [1] 6.172434
## [1] Error sd Value:
## [1] 4.125639
## [1] Pairs:
## [[1]]
## [1] 4 1
##
## [[2]]
## [1] 1 8
##
## [[3]]
## [1] 4 8
##
## [[4]]
## [1] 1 9
##
## [[5]]
## [1] 2 9
##
## [[6]]
## [1] 3 9
##
## [[7]]
## [1] 4 9
##
## [[8]]
## [1] 6 9
##
## [[9]]
## [1] 7 9
##
## [[10]]
## [1] 4 10
##
## [[11]]
## [1] 3 11
##
## [[12]]
## [1] 4 11
##
## [[13]]
## [1] 5 11
##
## [[14]]
## [1] 5 12
##
## [[15]]
## [1] 6 12
##
## [[16]]
## [1] 7 12
##
## [1]
## [1]gyb.res <- standard.sensitivity.plot(gy.dat,
response = response,
TreatmentName = TreatmentName,
TrialName = TrialName,
RepName=RepName,
outliers=2,
method="ave",
plot.outliers=TRUE,legend.columns=3)
gy2.res <- standard.sensitivity.plot(gy.dat,
response = response,
TreatmentName = TreatmentName,
TrialName = TrialName,
RepName=RepName,
outliers=2.4,
dual.dendrogram=TRUE,
method="ave",
plot.outliers=TRUE,legend.columns=3)
means.matrix <- tapply(gy.dat$Plot.Mean,list(gy.dat$Criteria.Entry.No.,gy.dat$Expt.No.),mean)
decomp <- decompose.means.table(means.matrix)
txt.matrix <- decomp$gamma
mean(unlist(txt.matrix),na.rm=TRUE)## [1] -2.095165e-15max <- sd(unlist(txt.matrix),na.rm=TRUE)
norm.mat <- abs(txt.matrix)
crit <- 3*max
gy.means$means.table - means.matrix## CPT:2007:Bison CPT:2007:Brookings CPT:2007:DLakesPea CPT:2007:Hayes
## 01 5.258016e-13 -1.492140e-13 8.526513e-14 -1.776357e-13
## 02 4.618528e-13 -1.065814e-13 0.000000e+00 -2.131628e-13
## 03 3.055334e-13 -4.973799e-14 1.492140e-13 -1.492140e-13
## 04 6.679102e-13 5.329071e-14 7.105427e-15 -1.563194e-13
## 05 -3.410605e-13 -1.065814e-13 -7.105427e-15 -2.273737e-13
## 06 1.186606e-12 -1.421085e-13 5.684342e-14 -2.344791e-13
## 07 0.000000e+00 -1.421085e-14 0.000000e+00 -2.060574e-13
## 08 4.263256e-14 -5.684342e-14 7.105427e-14 -1.847411e-13
## 09 2.131628e-14 -2.842171e-14 8.526513e-14 -1.634248e-13
## 10 4.192202e-13 -9.237056e-14 4.263256e-14 -1.847411e-13
## 11 1.421085e-13 -8.526513e-14 1.136868e-13 -1.634248e-13
## 12 2.060574e-13 -1.065814e-13 3.552714e-14 -1.918465e-13
## CPT:2007:Kennebec CPT:2007:Martin CPT:2007:Onida CPT:2007:Platte
## 01 1.705303e-13 -4.725109e-13 1.989520e-13 2.557954e-13
## 02 3.197442e-13 -4.689582e-13 1.989520e-13 2.629008e-13
## 03 3.836931e-13 -4.121148e-13 2.131628e-13 2.273737e-13
## 04 4.369838e-13 -3.907985e-13 2.913225e-13 2.486900e-13
## 05 3.197442e-13 -4.547474e-13 2.060574e-13 2.415845e-13
## 06 3.481659e-13 -4.263256e-13 2.202682e-13 2.415845e-13
## 07 3.836931e-13 -4.547474e-13 2.771117e-13 2.842171e-13
## 08 3.623768e-13 -4.760636e-13 1.918465e-13 2.984279e-13
## 09 4.121148e-13 -4.263256e-13 2.486900e-13 2.984279e-13
## 10 3.979039e-13 -4.547474e-13 2.344791e-13 2.344791e-13
## 11 3.979039e-13 -4.405365e-13 2.202682e-13 2.984279e-13
## 12 3.907985e-13 -4.689582e-13 2.415845e-13 2.984279e-13
## CPT:2007:Selby CPT:2007:Sturgis CPT:2007:Wall CPT:2007:Watertown
## 01 2.415845e-13 4.263256e-14 1.776357e-13 1.492140e-13
## 02 1.705303e-13 -4.263256e-14 1.421085e-13 1.350031e-13
## 03 3.055334e-13 -2.842171e-14 1.563194e-13 1.847411e-13
## 04 2.167155e-13 1.065814e-14 2.202682e-13 2.273737e-13
## 05 1.847411e-13 -6.394885e-14 1.634248e-13 1.705303e-13
## 06 2.557954e-13 -4.973799e-14 1.918465e-13 1.705303e-13
## 07 2.486900e-13 -3.197442e-14 1.207923e-13 1.350031e-13
## 08 2.060574e-13 -2.486900e-14 1.563194e-13 1.563194e-13
## 09 2.415845e-13 -1.065814e-14 1.989520e-13 2.202682e-13
## 10 2.486900e-13 -2.131628e-14 1.705303e-13 1.918465e-13
## 11 2.273737e-13 -7.105427e-15 1.705303e-13 2.131628e-13
## 12 2.486900e-13 -1.421085e-14 1.918465e-13 1.989520e-13
## CPT:2007:Winner
## 01 1.421085e-13
## 02 1.492140e-13
## 03 1.563194e-13
## 04 2.060574e-13
## 05 1.421085e-13
## 06 1.705303e-13
## 07 1.421085e-13
## 08 1.705303e-13
## 09 1.847411e-13
## 10 1.634248e-13
## 11 1.918465e-13
## 12 2.060574e-13gy.res$cluster$score/gy.res$add.cluster$score## [1] 1.3927179 1.2256504 1.0296006 1.2545966 0.9612729 0.9204759
## [7] 0.8777299 0.2101115 0.4671611 77.5177481 0.9335306 0.9547280gy.res$cluster$clusters## [1] 1 1 2 1 1 3 2 2 2 3 1 1 1gy.res$add.cluster$clusters## 1 2 3 4 5 6 7 8 9 10 11 12 13
## 1 2 1 1 2 3 1 1 1 3 2 2 2gy.res$cluster$means.hc$height## [1] 12.75757 16.01341 19.38159 19.76017 23.51187 24.68833 26.87330
## [8] 34.53185 34.78175 47.55139 62.18434 103.29494gy.res$add.cluster$means.hc$height## [1] 2.215582 2.680709 2.950214 6.256760 6.350852 7.127390 9.175900
## [8] 14.960588 18.080083 19.759451 50.149726 95.406771gy.res$cluster$means.hc$height/gy.res$add.cluster$means.hc$height## [1] 5.758115 5.973574 6.569555 3.158211 3.702160 3.463867 2.928683
## [8] 2.308188 1.923760 2.406514 1.239974 1.082679gy.res$cluster$means.hc$order## [1] 6 10 9 8 3 7 11 1 4 12 5 2 13gy.res$add.cluster$means.hc$order## [1] 6 10 11 12 13 2 5 4 1 9 8 3 7gy.res$cluster$means.hc$merge## [,1] [,2]
## [1,] -2 -13
## [2,] -1 -4
## [3,] -3 -7
## [4,] -5 1
## [5,] -6 -10
## [6,] -8 3
## [7,] -11 2
## [8,] -9 6
## [9,] -12 4
## [10,] 7 9
## [11,] 8 10
## [12,] 5 11gy.res$add.cluster$means.hc$merge## [,1] [,2]
## [1,] -1 -9
## [2,] -3 -7
## [3,] -2 -5
## [4,] -11 -12
## [5,] -4 1
## [6,] -13 3
## [7,] -8 2
## [8,] -6 -10
## [9,] 4 6
## [10,] 5 7
## [11,] 9 10
## [12,] 8 11gy.res$cluster$means.hc$merge==gy.res$add.cluster$means.hc$merge## [,1] [,2]
## [1,] FALSE FALSE
## [2,] FALSE FALSE
## [3,] FALSE FALSE
## [4,] FALSE FALSE
## [5,] FALSE FALSE
## [6,] FALSE TRUE
## [7,] FALSE TRUE
## [8,] FALSE FALSE
## [9,] FALSE FALSE
## [10,] FALSE FALSE
## [11,] FALSE TRUE
## [12,] FALSE TRUEtdf.tbl <- anova(gy.res$tdf$multiplicative.lm)
aov.tbl <- gy.res$aov
tdf.tbl## Analysis of Variance Table
##
## Response: Plot.Mean
## Df Sum Sq Mean Sq F value Pr(>F)
## Criteria.Entry.No. 11 17509 1591.7 34.1373 < 2.2e-16 ***
## Expt.No. 12 39698 3308.2 70.9513 < 2.2e-16 ***
## Expt.No.:Rep.No. 39 3998 102.5 2.1988 6.139e-05 ***
## eCriteria.Entry.No.:eExpt.No. 1 1155 1155.3 24.7774 8.579e-07 ***
## Residuals 560 26111 46.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1aov.tbl## Analysis of Variance Table
##
## Response: Plot.Mean
## Df Sum Sq Mean Sq F value Pr(>F)
## Expt.No. 12 39698 3308.2 194.3594 < 2.2e-16 ***
## Criteria.Entry.No. 11 17509 1591.7 93.5136 < 2.2e-16 ***
## Expt.No.:Criteria.Entry.No. 132 19964 151.2 8.8856 < 2.2e-16 ***
## Expt.No.:Rep.No. 39 3998 102.5 6.0232 < 2.2e-16 ***
## Residuals 429 7302 17.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1recompute.tdf.aov(tdf.tbl,aov.tbl)## Analysis of Variance Table
##
## Response: Plot.Mean
## Df Sum Sq Mean Sq F value Pr(>F)
## Criteria.Entry.No. 11 17509 1591.7 34.1373 < 2.2e-16 ***
## Expt.No. 12 39698 3308.2 70.9513 < 2.2e-16 ***
## Expt.No.:Rep.No. 39 3998 102.5 2.1988 6.139e-05 ***
## eCriteria.Entry.No.:eExpt.No. 1 1155 1155.3 15.4416 0.0003478 ***
## Residuals 38 2843 74.8 4.3955 1.044e-14 ***
## Residuals1 429 7302 17.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1last = dim(aov.tbl)[1] last.row <- aov.tbl[last,] tdf.last <- dim(tdf.tbl)[1] colnames(last.row) <- colnames(tdf.tbl) tdf.tbl <- rbind(tdf.tbl,last.row) #compute interaction residuals by subtracting 1df row from txt row tdf.tbl[tdf.last,] <- aov.tbl[last-1,] - tdf.tbl[tdf.last-1,] #recompute residuals tdf.tbl[tdf.last,3] <- tdf.tbl[tdf.last,2]/tdf.tbl[tdf.last,1] #test treatment:trial against interaction residual tdf.tbl[tdf.last-1,4] <- tdf.tbl[tdf.last-1,3]/tdf.tbl[tdf.last,3] tdf.tbl[tdf.last-1,5] <- 1-pf(tdf.tbl[tdf.last-1,4],tdf.tbl[tdf.last-1,1],tdf.tbl[tdf.last,1])
#test interaction residual against experimental residual tdf.tbl[tdf.last,4] <- tdf.tbl[tdf.last,3]/last.row[3] tdf.tbl[tdf.last,5] <- 1-pf(tdf.tbl[tdf.last,4],tdf.tbl[tdf.last,1],as.numeric(last.row[1]))
return(tdf.tbl)
```
tw.res <- standard.sensitivity.plot(tw.dat,
response = response,
TreatmentName = TreatmentName,
TrialName = TrialName,
RepName=RepName,
dual.dendrogram=TRUE,
plot.outliers=TRUE,legend.columns=3)
print.stdplot(tw.res)## [1] AOV
## [1] ----------------------------------------------------
## Analysis of Variance Table
##
## Response: Plot.Mean
## Df Sum Sq Mean Sq F value Pr(>F)
## Expt.No. 12 4128.3 344.02 168.3919 < 2.2e-16 ***
## Criteria.Entry.No. 11 1880.3 170.93 83.6688 < 2.2e-16 ***
## Expt.No.:Criteria.Entry.No. 132 1631.9 12.36 6.0515 < 2.2e-16 ***
## Expt.No.:Rep.No. 39 301.6 7.73 3.7854 4.862e-12 ***
## Residuals 425 868.3 2.04
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] Mixed Model
## [1] ----------------------------------------------------
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## Plot.Mean ~ Expt.No. + (1 | Expt.No./Rep.No.) + (1 | Expt.No.:Criteria.Entry.No.)
## Data: plot.dat
##
## REML criterion at convergence: 2613.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4708 -0.4532 0.0575 0.5014 2.6909
##
## Random effects:
## Groups Name Variance Std.Dev.
## Expt.No.:Criteria.Entry.No. (Intercept) 5.6585 2.3788
## Rep.No.:Expt.No. (Intercept) 0.4781 0.6914
## Expt.No. (Intercept) 7.4666 2.7325
## Residual 2.0446 1.4299
## Number of obs: 620, groups:
## Expt.No.:Criteria.Entry.No., 156; Rep.No.:Expt.No., 52; Expt.No., 13
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 63.073 2.846 22.161
## Expt.No.CPT:2007:Brookings -5.383 4.026 -1.337
## Expt.No.CPT:2007:DLakesPea -5.985 4.025 -1.487
## Expt.No.CPT:2007:Hayes -1.221 4.025 -0.303
## Expt.No.CPT:2007:Kennebec -9.357 4.025 -2.325
## Expt.No.CPT:2007:Martin -3.223 4.025 -0.801
## Expt.No.CPT:2007:Onida -5.841 4.025 -1.451
## Expt.No.CPT:2007:Platte -7.438 4.025 -1.848
## Expt.No.CPT:2007:Selby -7.150 4.025 -1.776
## Expt.No.CPT:2007:Sturgis -1.927 4.025 -0.479
## Expt.No.CPT:2007:Wall -4.103 4.025 -1.019
## Expt.No.CPT:2007:Watertown -3.102 4.025 -0.771
## Expt.No.CPT:2007:Winner -5.661 4.025 -1.407
##
## Correlation of Fixed Effects:
## (Intr) E.N.CPT:2007:B E.N.CPT:2007:D E.N.CPT:2007:H
## E.N.CPT:2007:B -0.707
## E.N.CPT:2007:D -0.707 0.500
## E.N.CPT:2007:H -0.707 0.500 0.500
## E.N.CPT:2007:K -0.707 0.500 0.500 0.500
## E.N.CPT:2007:M -0.707 0.500 0.500 0.500
## E.N.CPT:2007:O -0.707 0.500 0.500 0.500
## E.N.CPT:2007:P -0.707 0.500 0.500 0.500
## Expt.N.CPT:2007:Sl -0.707 0.500 0.500 0.500
## Expt.N.CPT:2007:St -0.707 0.500 0.500 0.500
## Expt.N.CPT:2007:Wl -0.707 0.500 0.500 0.500
## Expt.N.CPT:2007:Wt -0.707 0.500 0.500 0.500
## Expt.N.CPT:2007:Wn -0.707 0.500 0.500 0.500
## E.N.CPT:2007:K E.N.CPT:2007:M E.N.CPT:2007:O
## E.N.CPT:2007:B
## E.N.CPT:2007:D
## E.N.CPT:2007:H
## E.N.CPT:2007:K
## E.N.CPT:2007:M 0.500
## E.N.CPT:2007:O 0.500 0.500
## E.N.CPT:2007:P 0.500 0.500 0.500
## Expt.N.CPT:2007:Sl 0.500 0.500 0.500
## Expt.N.CPT:2007:St 0.500 0.500 0.500
## Expt.N.CPT:2007:Wl 0.500 0.500 0.500
## Expt.N.CPT:2007:Wt 0.500 0.500 0.500
## Expt.N.CPT:2007:Wn 0.500 0.500 0.500
## E.N.CPT:2007:P Expt.N.CPT:2007:Sl Expt.N.CPT:2007:St
## E.N.CPT:2007:B
## E.N.CPT:2007:D
## E.N.CPT:2007:H
## E.N.CPT:2007:K
## E.N.CPT:2007:M
## E.N.CPT:2007:O
## E.N.CPT:2007:P
## Expt.N.CPT:2007:Sl 0.500
## Expt.N.CPT:2007:St 0.500 0.500
## Expt.N.CPT:2007:Wl 0.500 0.500 0.500
## Expt.N.CPT:2007:Wt 0.500 0.500 0.500
## Expt.N.CPT:2007:Wn 0.500 0.500 0.500
## Expt.N.CPT:2007:Wl Expt.N.CPT:2007:Wt
## E.N.CPT:2007:B
## E.N.CPT:2007:D
## E.N.CPT:2007:H
## E.N.CPT:2007:K
## E.N.CPT:2007:M
## E.N.CPT:2007:O
## E.N.CPT:2007:P
## Expt.N.CPT:2007:Sl
## Expt.N.CPT:2007:St
## Expt.N.CPT:2007:Wl
## Expt.N.CPT:2007:Wt 0.500
## Expt.N.CPT:2007:Wn 0.500 0.500
## [1]
## [1] Stability
## [1] ----------------------------------------------------
## Treatment Slope Intercept Mean SD b
## 1 1 1.0236239 -0.6044865 59.19784 3.206041 0.023623936
## 2 2 0.5756150 25.4506800 59.07935 1.786156 -0.424385033
## 3 3 1.4578990 -27.7130516 57.46056 4.173253 0.457899021
## 4 4 1.6812709 -41.1163899 57.10709 4.908317 0.681270932
## 5 5 0.5923172 26.2070972 60.81155 2.060720 -0.407682801
## 6 6 0.5074963 30.9999788 60.64901 2.078896 -0.492503662
## 7 7 1.0076033 0.4871182 59.35348 3.057007 0.007603260
## 8 8 1.3668135 -24.4649125 55.38728 3.797264 0.366813461
## 9 9 0.5600023 28.0259437 60.74249 1.988947 -0.439997720
## 10 10 1.0024725 -1.3033282 57.26328 2.898771 0.002472472
## 11 11 0.8397078 8.7686355 57.82618 2.473374 -0.160292179
## 12 12 1.3851783 -24.7372847 56.18782 3.860661 0.385178314
## Pb bR2
## 1 0.900928919 1.473171e-03
## 2 0.001438911 6.180498e-01
## 3 0.016789197 4.188968e-01
## 4 0.009307985 4.736197e-01
## 5 0.018238667 4.108484e-01
## 6 0.017495975 4.149003e-01
## 7 0.962931685 2.054539e-04
## 8 0.006547976 5.040948e-01
## 9 0.011892635 4.514403e-01
## 10 0.984054800 3.799709e-05
## 11 0.188308310 1.517212e-01
## 12 0.006984747 4.986186e-01
## [1]
## [1] Tukey's 1 d.f.
## [1] ----------------------------------------------------
##
## Call:
## lm(formula = as.formula(modelString), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.8885 -1.1662 0.0518 1.2642 5.2734
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 63.77828 0.63309 100.741 < 2e-16
## Criteria.Entry.No.02 -0.11849 0.39073 -0.303 0.761821
## Criteria.Entry.No.03 -1.73728 0.39073 -4.446 1.06e-05
## Criteria.Entry.No.04 -1.99783 0.39720 -5.030 6.64e-07
## Criteria.Entry.No.05 1.61371 0.39073 4.130 4.19e-05
## Criteria.Entry.No.06 1.45118 0.39073 3.714 0.000225
## Criteria.Entry.No.07 0.15564 0.39073 0.398 0.690536
## Criteria.Entry.No.08 -3.81055 0.39073 -9.752 < 2e-16
## Criteria.Entry.No.09 1.54465 0.39073 3.953 8.71e-05
## Criteria.Entry.No.10 -1.93456 0.39073 -4.951 9.80e-07
## Criteria.Entry.No.11 -1.39586 0.39282 -3.553 0.000413
## Criteria.Entry.No.12 -3.01001 0.39073 -7.704 6.11e-14
## Expt.No.CPT:2007:Brookings -3.61917 0.81337 -4.450 1.04e-05
## Expt.No.CPT:2007:DLakesPea -5.21625 0.81337 -6.413 3.05e-10
## Expt.No.CPT:2007:Hayes -1.45000 0.81337 -1.783 0.075181
## Expt.No.CPT:2007:Kennebec -7.98293 0.83202 -9.595 < 2e-16
## Expt.No.CPT:2007:Martin -3.15000 0.81337 -3.873 0.000120
## Expt.No.CPT:2007:Onida -5.79750 0.81337 -7.128 3.18e-12
## Expt.No.CPT:2007:Platte -6.18250 0.81337 -7.601 1.26e-13
## Expt.No.CPT:2007:Selby -7.44708 0.81337 -9.156 < 2e-16
## Expt.No.CPT:2007:Sturgis -2.25833 0.81337 -2.777 0.005680
## Expt.No.CPT:2007:Wall -4.07268 0.81337 -5.007 7.43e-07
## Expt.No.CPT:2007:Watertown -1.92708 0.81337 -2.369 0.018165
## Expt.No.CPT:2007:Winner -5.66875 0.81337 -6.969 9.04e-12
## Expt.No.CPT:2007:Bison:Rep.No.2 -0.85833 0.81337 -1.055 0.291757
## Expt.No.CPT:2007:Brookings:Rep.No.2 -1.71124 0.83202 -2.057 0.040179
## Expt.No.CPT:2007:DLakesPea:Rep.No.2 -0.03417 0.81337 -0.042 0.966509
## Expt.No.CPT:2007:Hayes:Rep.No.2 0.03333 0.81337 0.041 0.967325
## Expt.No.CPT:2007:Kennebec:Rep.No.2 -3.53691 0.83202 -4.251 2.50e-05
## Expt.No.CPT:2007:Martin:Rep.No.2 0.72500 0.81337 0.891 0.373127
## Expt.No.CPT:2007:Onida:Rep.No.2 0.11000 0.81337 0.135 0.892472
## Expt.No.CPT:2007:Platte:Rep.No.2 -1.34542 0.81337 -1.654 0.098668
## Expt.No.CPT:2007:Selby:Rep.No.2 0.65708 0.81337 0.808 0.419522
## Expt.No.CPT:2007:Sturgis:Rep.No.2 1.17500 0.81337 1.445 0.149133
## Expt.No.CPT:2007:Wall:Rep.No.2 0.49103 0.81337 0.604 0.546290
## Expt.No.CPT:2007:Watertown:Rep.No.2 -1.62250 0.81337 -1.995 0.046554
## Expt.No.CPT:2007:Winner:Rep.No.2 0.11583 0.81337 0.142 0.886807
## Expt.No.CPT:2007:Bison:Rep.No.3 -0.27500 0.81337 -0.338 0.735416
## Expt.No.CPT:2007:Brookings:Rep.No.3 -2.69670 0.83202 -3.241 0.001262
## Expt.No.CPT:2007:DLakesPea:Rep.No.3 -1.42500 0.81337 -1.752 0.080331
## Expt.No.CPT:2007:Hayes:Rep.No.3 0.85000 0.81337 1.045 0.296462
## Expt.No.CPT:2007:Kennebec:Rep.No.3 -1.53941 0.83202 -1.850 0.064814
## Expt.No.CPT:2007:Martin:Rep.No.3 0.10833 0.81337 0.133 0.894091
## Expt.No.CPT:2007:Onida:Rep.No.3 -0.59667 0.81337 -0.734 0.463519
## Expt.No.CPT:2007:Platte:Rep.No.3 -1.85417 0.81337 -2.280 0.023009
## Expt.No.CPT:2007:Selby:Rep.No.3 -0.13292 0.81337 -0.163 0.870253
## Expt.No.CPT:2007:Sturgis:Rep.No.3 0.39167 0.81337 0.482 0.630326
## Expt.No.CPT:2007:Wall:Rep.No.3 -0.31208 0.81337 -0.384 0.701358
## Expt.No.CPT:2007:Watertown:Rep.No.3 -1.45333 0.81337 -1.787 0.074514
## Expt.No.CPT:2007:Winner:Rep.No.3 -0.06000 0.81337 -0.074 0.941223
## Expt.No.CPT:2007:Bison:Rep.No.4 1.39167 0.81337 1.711 0.087642
## Expt.No.CPT:2007:Brookings:Rep.No.4 -2.30397 0.83202 -2.769 0.005808
## Expt.No.CPT:2007:DLakesPea:Rep.No.4 -1.35875 0.81337 -1.671 0.095381
## Expt.No.CPT:2007:Hayes:Rep.No.4 0.29167 0.81337 0.359 0.720039
## Expt.No.CPT:2007:Kennebec:Rep.No.4 -0.22341 0.83202 -0.269 0.788404
## Expt.No.CPT:2007:Martin:Rep.No.4 -0.86667 0.81337 -1.066 0.287102
## Expt.No.CPT:2007:Onida:Rep.No.4 0.57250 0.81337 0.704 0.481815
## Expt.No.CPT:2007:Platte:Rep.No.4 -1.56542 0.81337 -1.925 0.054789
## Expt.No.CPT:2007:Selby:Rep.No.4 0.92250 0.81337 1.134 0.257213
## Expt.No.CPT:2007:Sturgis:Rep.No.4 0.01667 0.81337 0.020 0.983658
## Expt.No.CPT:2007:Wall:Rep.No.4 -0.04230 0.81337 -0.052 0.958543
## Expt.No.CPT:2007:Watertown:Rep.No.4 -1.36417 0.81337 -1.677 0.094070
## Expt.No.CPT:2007:Winner:Rep.No.4 0.23208 0.81337 0.285 0.775494
## eCriteria.Entry.No.:eExpt.No. -0.17107 0.01979 -8.645 < 2e-16
##
## (Intercept) ***
## Criteria.Entry.No.02
## Criteria.Entry.No.03 ***
## Criteria.Entry.No.04 ***
## Criteria.Entry.No.05 ***
## Criteria.Entry.No.06 ***
## Criteria.Entry.No.07
## Criteria.Entry.No.08 ***
## Criteria.Entry.No.09 ***
## Criteria.Entry.No.10 ***
## Criteria.Entry.No.11 ***
## Criteria.Entry.No.12 ***
## Expt.No.CPT:2007:Brookings ***
## Expt.No.CPT:2007:DLakesPea ***
## Expt.No.CPT:2007:Hayes .
## Expt.No.CPT:2007:Kennebec ***
## Expt.No.CPT:2007:Martin ***
## Expt.No.CPT:2007:Onida ***
## Expt.No.CPT:2007:Platte ***
## Expt.No.CPT:2007:Selby ***
## Expt.No.CPT:2007:Sturgis **
## Expt.No.CPT:2007:Wall ***
## Expt.No.CPT:2007:Watertown *
## Expt.No.CPT:2007:Winner ***
## Expt.No.CPT:2007:Bison:Rep.No.2
## Expt.No.CPT:2007:Brookings:Rep.No.2 *
## Expt.No.CPT:2007:DLakesPea:Rep.No.2
## Expt.No.CPT:2007:Hayes:Rep.No.2
## Expt.No.CPT:2007:Kennebec:Rep.No.2 ***
## Expt.No.CPT:2007:Martin:Rep.No.2
## Expt.No.CPT:2007:Onida:Rep.No.2
## Expt.No.CPT:2007:Platte:Rep.No.2 .
## Expt.No.CPT:2007:Selby:Rep.No.2
## Expt.No.CPT:2007:Sturgis:Rep.No.2
## Expt.No.CPT:2007:Wall:Rep.No.2
## Expt.No.CPT:2007:Watertown:Rep.No.2 *
## Expt.No.CPT:2007:Winner:Rep.No.2
## Expt.No.CPT:2007:Bison:Rep.No.3
## Expt.No.CPT:2007:Brookings:Rep.No.3 **
## Expt.No.CPT:2007:DLakesPea:Rep.No.3 .
## Expt.No.CPT:2007:Hayes:Rep.No.3
## Expt.No.CPT:2007:Kennebec:Rep.No.3 .
## Expt.No.CPT:2007:Martin:Rep.No.3
## Expt.No.CPT:2007:Onida:Rep.No.3
## Expt.No.CPT:2007:Platte:Rep.No.3 *
## Expt.No.CPT:2007:Selby:Rep.No.3
## Expt.No.CPT:2007:Sturgis:Rep.No.3
## Expt.No.CPT:2007:Wall:Rep.No.3
## Expt.No.CPT:2007:Watertown:Rep.No.3 .
## Expt.No.CPT:2007:Winner:Rep.No.3
## Expt.No.CPT:2007:Bison:Rep.No.4 .
## Expt.No.CPT:2007:Brookings:Rep.No.4 **
## Expt.No.CPT:2007:DLakesPea:Rep.No.4 .
## Expt.No.CPT:2007:Hayes:Rep.No.4
## Expt.No.CPT:2007:Kennebec:Rep.No.4
## Expt.No.CPT:2007:Martin:Rep.No.4
## Expt.No.CPT:2007:Onida:Rep.No.4
## Expt.No.CPT:2007:Platte:Rep.No.4 .
## Expt.No.CPT:2007:Selby:Rep.No.4
## Expt.No.CPT:2007:Sturgis:Rep.No.4
## Expt.No.CPT:2007:Wall:Rep.No.4
## Expt.No.CPT:2007:Watertown:Rep.No.4 .
## Expt.No.CPT:2007:Winner:Rep.No.4
## eCriteria.Entry.No.:eExpt.No. ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.992 on 556 degrees of freedom
## Multiple R-squared: 0.7495, Adjusted R-squared: 0.7211
## F-statistic: 26.41 on 63 and 556 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## Criteria.Entry.No. 11 1870 170.0 42.823 < 2e-16 ***
## Expt.No. 12 4139 344.9 86.887 < 2e-16 ***
## Expt.No.:Rep.No. 39 298 7.6 1.926 0.000836 ***
## eCriteria.Entry.No.:eExpt.No. 1 297 296.6 74.733 < 2e-16 ***
## Residuals 556 2207 4.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1]
## [1] Heterogenous Slopes
## [1] ----------------------------------------------------
##
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.3509 -1.2308 0.0412 1.2838 5.5421
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 65.329779 0.766828 85.195 < 2e-16
## Criteria.Entry.No.02 -0.118485 0.376960 -0.314 0.753400
## Criteria.Entry.No.03 -1.737277 0.376960 -4.609 5.05e-06
## Criteria.Entry.No.04 -1.979005 0.383228 -5.164 3.39e-07
## Criteria.Entry.No.05 1.613712 0.376960 4.281 2.20e-05
## Criteria.Entry.No.06 1.451176 0.376960 3.850 0.000132
## Criteria.Entry.No.07 0.155642 0.376960 0.413 0.679851
## Criteria.Entry.No.08 -3.810553 0.376960 -10.109 < 2e-16
## Criteria.Entry.No.09 1.544652 0.376960 4.098 4.81e-05
## Criteria.Entry.No.10 -1.934556 0.376960 -5.132 3.99e-07
## Criteria.Entry.No.11 -1.368763 0.379078 -3.611 0.000333
## Criteria.Entry.No.12 -3.010010 0.376960 -7.985 8.36e-15
## Expt.No.CPT:2007:Brookings -4.956078 0.880126 -5.631 2.87e-08
## Expt.No.CPT:2007:DLakesPea -7.143120 0.972503 -7.345 7.53e-13
## Expt.No.CPT:2007:Hayes -1.985627 0.800789 -2.480 0.013454
## Expt.No.CPT:2007:Kennebec -10.800853 1.185433 -9.111 < 2e-16
## Expt.No.CPT:2007:Martin -4.313602 0.857965 -5.028 6.74e-07
## Expt.No.CPT:2007:Onida -7.939082 1.011634 -7.848 2.25e-14
## Expt.No.CPT:2007:Platte -8.466301 1.038914 -8.149 2.51e-15
## Expt.No.CPT:2007:Selby -10.198019 1.135068 -8.985 < 2e-16
## Expt.No.CPT:2007:Sturgis -3.092557 0.823175 -3.757 0.000191
## Expt.No.CPT:2007:Wall -5.577122 0.903842 -6.170 1.33e-09
## Expt.No.CPT:2007:Watertown -2.638942 0.812898 -3.246 0.001241
## Expt.No.CPT:2007:Winner -7.762773 1.002745 -7.742 4.79e-14
## Expt.No.CPT:2007:Bison:Rep.No.2 -0.858333 0.784706 -1.094 0.274513
## Expt.No.CPT:2007:Brookings:Rep.No.2 -1.684051 0.802722 -2.098 0.036371
## Expt.No.CPT:2007:DLakesPea:Rep.No.2 -0.034167 0.784706 -0.044 0.965286
## Expt.No.CPT:2007:Hayes:Rep.No.2 0.033333 0.784706 0.042 0.966132
## Expt.No.CPT:2007:Kennebec:Rep.No.2 -3.654318 0.803637 -4.547 6.70e-06
## Expt.No.CPT:2007:Martin:Rep.No.2 0.725000 0.784706 0.924 0.355940
## Expt.No.CPT:2007:Onida:Rep.No.2 0.110000 0.784706 0.140 0.888570
## Expt.No.CPT:2007:Platte:Rep.No.2 -1.345416 0.784706 -1.715 0.086996
## Expt.No.CPT:2007:Selby:Rep.No.2 0.657084 0.784706 0.837 0.402754
## Expt.No.CPT:2007:Sturgis:Rep.No.2 1.175000 0.784706 1.497 0.134873
## Expt.No.CPT:2007:Wall:Rep.No.2 0.491033 0.784706 0.626 0.531738
## Expt.No.CPT:2007:Watertown:Rep.No.2 -1.622501 0.784706 -2.068 0.039143
## Expt.No.CPT:2007:Winner:Rep.No.2 0.115834 0.784706 0.148 0.882702
## Expt.No.CPT:2007:Bison:Rep.No.3 -0.275000 0.784706 -0.350 0.726136
## Expt.No.CPT:2007:Brookings:Rep.No.3 -2.669507 0.802722 -3.326 0.000942
## Expt.No.CPT:2007:DLakesPea:Rep.No.3 -1.425000 0.784706 -1.816 0.069924
## Expt.No.CPT:2007:Hayes:Rep.No.3 0.850000 0.784706 1.083 0.279194
## Expt.No.CPT:2007:Kennebec:Rep.No.3 -1.656819 0.803637 -2.062 0.039714
## Expt.No.CPT:2007:Martin:Rep.No.3 0.108333 0.784706 0.138 0.890247
## Expt.No.CPT:2007:Onida:Rep.No.3 -0.596667 0.784706 -0.760 0.447361
## Expt.No.CPT:2007:Platte:Rep.No.3 -1.854167 0.784706 -2.363 0.018483
## Expt.No.CPT:2007:Selby:Rep.No.3 -0.132915 0.784706 -0.169 0.865558
## Expt.No.CPT:2007:Sturgis:Rep.No.3 0.391667 0.784706 0.499 0.617892
## Expt.No.CPT:2007:Wall:Rep.No.3 -0.312079 0.784706 -0.398 0.691005
## Expt.No.CPT:2007:Watertown:Rep.No.3 -1.453334 0.784706 -1.852 0.064554
## Expt.No.CPT:2007:Winner:Rep.No.3 -0.060000 0.784706 -0.076 0.939080
## Expt.No.CPT:2007:Bison:Rep.No.4 1.391667 0.784706 1.773 0.076705
## Expt.No.CPT:2007:Brookings:Rep.No.4 -2.276780 0.802722 -2.836 0.004733
## Expt.No.CPT:2007:DLakesPea:Rep.No.4 -1.358750 0.784706 -1.732 0.083920
## Expt.No.CPT:2007:Hayes:Rep.No.4 0.291667 0.784706 0.372 0.710268
## Expt.No.CPT:2007:Kennebec:Rep.No.4 -0.340819 0.803637 -0.424 0.671663
## Expt.No.CPT:2007:Martin:Rep.No.4 -0.866667 0.784706 -1.104 0.269885
## Expt.No.CPT:2007:Onida:Rep.No.4 0.572500 0.784706 0.730 0.465964
## Expt.No.CPT:2007:Platte:Rep.No.4 -1.565417 0.784706 -1.995 0.046549
## Expt.No.CPT:2007:Selby:Rep.No.4 0.922501 0.784706 1.176 0.240267
## Expt.No.CPT:2007:Sturgis:Rep.No.4 0.016668 0.784706 0.021 0.983061
## Expt.No.CPT:2007:Wall:Rep.No.4 -0.042300 0.784706 -0.054 0.957030
## Expt.No.CPT:2007:Watertown:Rep.No.4 -1.364168 0.784706 -1.738 0.082696
## Expt.No.CPT:2007:Winner:Rep.No.4 0.232084 0.784706 0.296 0.767526
## Criteria.Entry.No.01:eExpt.No. -0.372964 0.162631 -2.293 0.022209
## Criteria.Entry.No.02:eExpt.No. -0.831938 0.162631 -5.115 4.34e-07
## Criteria.Entry.No.03:eExpt.No. 0.140961 0.162631 0.867 0.386459
## Criteria.Entry.No.04:eExpt.No. 0.322587 0.162808 1.981 0.048049
## Criteria.Entry.No.05:eExpt.No. -0.758626 0.162631 -4.665 3.89e-06
## Criteria.Entry.No.06:eExpt.No. -0.805206 0.162631 -4.951 9.85e-07
## Criteria.Entry.No.07:eExpt.No. -0.422445 0.162631 -2.598 0.009642
## Criteria.Entry.No.08:eExpt.No. -0.005928 0.162631 -0.036 0.970936
## Criteria.Entry.No.09:eExpt.No. -0.723582 0.162631 -4.449 1.04e-05
## Criteria.Entry.No.10:eExpt.No. -0.372377 0.162631 -2.290 0.022419
## Criteria.Entry.No.11:eExpt.No. -0.603251 0.164994 -3.656 0.000281
## Criteria.Entry.No.12:eExpt.No. NA NA NA NA
##
## (Intercept) ***
## Criteria.Entry.No.02
## Criteria.Entry.No.03 ***
## Criteria.Entry.No.04 ***
## Criteria.Entry.No.05 ***
## Criteria.Entry.No.06 ***
## Criteria.Entry.No.07
## Criteria.Entry.No.08 ***
## Criteria.Entry.No.09 ***
## Criteria.Entry.No.10 ***
## Criteria.Entry.No.11 ***
## Criteria.Entry.No.12 ***
## Expt.No.CPT:2007:Brookings ***
## Expt.No.CPT:2007:DLakesPea ***
## Expt.No.CPT:2007:Hayes *
## Expt.No.CPT:2007:Kennebec ***
## Expt.No.CPT:2007:Martin ***
## Expt.No.CPT:2007:Onida ***
## Expt.No.CPT:2007:Platte ***
## Expt.No.CPT:2007:Selby ***
## Expt.No.CPT:2007:Sturgis ***
## Expt.No.CPT:2007:Wall ***
## Expt.No.CPT:2007:Watertown **
## Expt.No.CPT:2007:Winner ***
## Expt.No.CPT:2007:Bison:Rep.No.2
## Expt.No.CPT:2007:Brookings:Rep.No.2 *
## Expt.No.CPT:2007:DLakesPea:Rep.No.2
## Expt.No.CPT:2007:Hayes:Rep.No.2
## Expt.No.CPT:2007:Kennebec:Rep.No.2 ***
## Expt.No.CPT:2007:Martin:Rep.No.2
## Expt.No.CPT:2007:Onida:Rep.No.2
## Expt.No.CPT:2007:Platte:Rep.No.2 .
## Expt.No.CPT:2007:Selby:Rep.No.2
## Expt.No.CPT:2007:Sturgis:Rep.No.2
## Expt.No.CPT:2007:Wall:Rep.No.2
## Expt.No.CPT:2007:Watertown:Rep.No.2 *
## Expt.No.CPT:2007:Winner:Rep.No.2
## Expt.No.CPT:2007:Bison:Rep.No.3
## Expt.No.CPT:2007:Brookings:Rep.No.3 ***
## Expt.No.CPT:2007:DLakesPea:Rep.No.3 .
## Expt.No.CPT:2007:Hayes:Rep.No.3
## Expt.No.CPT:2007:Kennebec:Rep.No.3 *
## Expt.No.CPT:2007:Martin:Rep.No.3
## Expt.No.CPT:2007:Onida:Rep.No.3
## Expt.No.CPT:2007:Platte:Rep.No.3 *
## Expt.No.CPT:2007:Selby:Rep.No.3
## Expt.No.CPT:2007:Sturgis:Rep.No.3
## Expt.No.CPT:2007:Wall:Rep.No.3
## Expt.No.CPT:2007:Watertown:Rep.No.3 .
## Expt.No.CPT:2007:Winner:Rep.No.3
## Expt.No.CPT:2007:Bison:Rep.No.4 .
## Expt.No.CPT:2007:Brookings:Rep.No.4 **
## Expt.No.CPT:2007:DLakesPea:Rep.No.4 .
## Expt.No.CPT:2007:Hayes:Rep.No.4
## Expt.No.CPT:2007:Kennebec:Rep.No.4
## Expt.No.CPT:2007:Martin:Rep.No.4
## Expt.No.CPT:2007:Onida:Rep.No.4
## Expt.No.CPT:2007:Platte:Rep.No.4 *
## Expt.No.CPT:2007:Selby:Rep.No.4
## Expt.No.CPT:2007:Sturgis:Rep.No.4
## Expt.No.CPT:2007:Wall:Rep.No.4
## Expt.No.CPT:2007:Watertown:Rep.No.4 .
## Expt.No.CPT:2007:Winner:Rep.No.4
## Criteria.Entry.No.01:eExpt.No. *
## Criteria.Entry.No.02:eExpt.No. ***
## Criteria.Entry.No.03:eExpt.No.
## Criteria.Entry.No.04:eExpt.No. *
## Criteria.Entry.No.05:eExpt.No. ***
## Criteria.Entry.No.06:eExpt.No. ***
## Criteria.Entry.No.07:eExpt.No. **
## Criteria.Entry.No.08:eExpt.No.
## Criteria.Entry.No.09:eExpt.No. ***
## Criteria.Entry.No.10:eExpt.No. *
## Criteria.Entry.No.11:eExpt.No. ***
## Criteria.Entry.No.12:eExpt.No.
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.922 on 546 degrees of freedom
## Multiple R-squared: 0.771, Adjusted R-squared: 0.7404
## F-statistic: 25.19 on 73 and 546 DF, p-value: < 2.2e-16
##
##
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.113 -1.148 0.172 1.295 4.875
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## Criteria.Entry.No.01 59.1978 0.2892 204.664 < 2e-16 ***
## Criteria.Entry.No.02 59.0793 0.2892 204.254 < 2e-16 ***
## Criteria.Entry.No.03 57.4606 0.2892 198.657 < 2e-16 ***
## Criteria.Entry.No.04 57.3348 0.2980 192.397 < 2e-16 ***
## Criteria.Entry.No.05 60.8115 0.2892 210.243 < 2e-16 ***
## Criteria.Entry.No.06 60.6490 0.2892 209.681 < 2e-16 ***
## Criteria.Entry.No.07 59.3535 0.2892 205.202 < 2e-16 ***
## Criteria.Entry.No.08 55.3873 0.2892 191.489 < 2e-16 ***
## Criteria.Entry.No.09 60.7425 0.2892 210.004 < 2e-16 ***
## Criteria.Entry.No.10 57.2633 0.2892 197.975 < 2e-16 ***
## Criteria.Entry.No.11 57.8142 0.2922 197.847 < 2e-16 ***
## Criteria.Entry.No.12 56.1878 0.2892 194.257 < 2e-16 ***
## Criteria.Entry.No.01:eExpt.No. 1.0689 0.1248 8.566 < 2e-16 ***
## Criteria.Entry.No.02:eExpt.No. 0.6099 0.1248 4.888 1.31e-06 ***
## Criteria.Entry.No.03:eExpt.No. 1.5828 0.1248 12.684 < 2e-16 ***
## Criteria.Entry.No.04:eExpt.No. 1.7772 0.1250 14.214 < 2e-16 ***
## Criteria.Entry.No.05:eExpt.No. 0.6832 0.1248 5.475 6.45e-08 ***
## Criteria.Entry.No.06:eExpt.No. 0.6367 0.1248 5.102 4.52e-07 ***
## Criteria.Entry.No.07:eExpt.No. 1.0194 0.1248 8.169 1.86e-15 ***
## Criteria.Entry.No.08:eExpt.No. 1.4359 0.1248 11.507 < 2e-16 ***
## Criteria.Entry.No.09:eExpt.No. 0.7183 0.1248 5.756 1.38e-08 ***
## Criteria.Entry.No.10:eExpt.No. 1.0695 0.1248 8.571 < 2e-16 ***
## Criteria.Entry.No.11:eExpt.No. 0.8489 0.1281 6.627 7.65e-11 ***
## Criteria.Entry.No.12:eExpt.No. 1.4419 0.1248 11.555 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.086 on 596 degrees of freedom
## Multiple R-squared: 0.9988, Adjusted R-squared: 0.9987
## F-statistic: 2.034e+04 on 24 and 596 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## Criteria.Entry.No. 12 2119788 176649 40604.79 <2e-16 ***
## Criteria.Entry.No.:eExpt.No. 12 4348 362 83.28 <2e-16 ***
## Residuals 596 2593 4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Criteria.Entry.No. 11 1870 170.0 46.009 < 2e-16 ***
## Expt.No. 12 4139 344.9 93.351 < 2e-16 ***
## Expt.No.:Rep.No. 39 298 7.6 2.069 0.000221 ***
## Criteria.Entry.No.:eExpt.No. 11 486 44.2 11.969 < 2e-16 ***
## Residuals 546 2017 3.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1]
## [1] Random Outliers
## [1] ----------------------------------------------------
## [1] Critical Value:
## [1] 4.287986
## [1] Interaction sd Value:
## [1] 1.769951
## [1] Error sd Value:
## [1] 1.429329
## [1] Pairs:
## [[1]]
## [1] 4 9
##
## [[2]]
## [1] 4 10
##
## [1]
## [1]tw.res$cluster$score/tw.res$add.cluster$score## [1] 1.2259743 0.9697232 0.9300447 0.8278188 0.8531172 0.8305974 0.8721844
## [8] 3.7475335 1.8639613 0.2142266 1.6386883 0.9722437tw.res <- standard.sensitivity.plot(tw.dat,
response = response,
TreatmentName = TreatmentName,
TrialName = TrialName,
RepName=RepName,
dual.dendrogram=FALSE,
plot.outliers=TRUE,legend.columns=3)
gy.matrix <- data.frame(tapply(gy.dat$Plot.Mean,list(gy.dat$Expt.No.,gy.dat$Criteria.Entry.No.),mean))
gy.vector <- tapply(gy.dat$Plot.Mean,list(gy.dat$Expt.No.),mean)
par(fig = c(0, 1, 0.4, 1), mar = (c(1, 4, 1, 1) + 0.1))
gy.table <- plot.interaction.ARMST(gy.matrix, gy.vector, ylab='Treatment in Trial Mean',
regression=TRUE, main='GY', show.legend=TRUE,legend.pos=c(.01,.98),
legend.columns=4,lwd = 1
)
par(fig=c(0,1,0,.4),mar=(c(5, 4, 0, 1) + 0.1), new=TRUE)
gy.hc <- plot.clusters.ARMST(gy.matrix, gy.vector, xlab='Trial Mean', ylab='')
par(fig = c(0, 1, 0, 1))
tw.matrix <- data.frame(tapply(tw.dat$Plot.Mean,list(tw.dat$Expt.No.,tw.dat$Criteria.Entry.No.),mean))
tw.vector <- tapply(tw.dat$Plot.Mean,list(tw.dat$Expt.No.),mean)
par(fig = c(0, 1, 0.4, 1), mar = (c(1, 4, 1, 1) + 0.1))
tw.table <- plot.interaction.ARMST(tw.matrix, tw.vector, ylab='Treatment in Trial Mean',
regression=TRUE, main='TW', show.legend=TRUE,legend.pos=c(.01,.98),
legend.columns=4,lwd = 1
)
par(fig=c(0,1,0,.4),mar=(c(5, 4, 0, 1) + 0.1), new=TRUE)
tw.hc <- plot.clusters.ARMST(tw.matrix, tw.vector, xlab='Trial Mean', ylab='')
par(fig = c(0, 1, 0, 1))
hd.matrix <- data.frame(tapply(hd.dat$Plot.Mean,list(hd.dat$Expt.No.,hd.dat$Criteria.Entry.No.),mean))
hd.vector <- tapply(hd.dat$Plot.Mean,list(hd.dat$Expt.No.),mean)
par(fig = c(0, 1, 0.4, 1), mar = (c(1, 4, 1, 1) + 0.1))
hd.table <- plot.interaction.ARMST(hd.matrix, hd.vector, ylab='Treatment in Trial Mean',
regression=TRUE, main='HD', show.legend=TRUE,legend.pos=c(.01,.98),
legend.columns=4,lwd = 1
)
par(fig=c(0,1,0,.4),mar=(c(5, 4, 0, 1) + 0.1), new=TRUE)
hd.hc <- plot.clusters.ARMST(hd.matrix, hd.vector, xlab='Trial Mean', ylab='')
par(fig = c(0, 1, 0, 1))
ht.matrix <- data.frame(tapply(ht.dat$Plot.Mean,list(ht.dat$Expt.No.,ht.dat$Criteria.Entry.No.),mean))
ht.vector <- tapply(ht.dat$Plot.Mean,list(ht.dat$Expt.No.),mean)
par(fig = c(0, 1, 0.4, 1), mar = (c(1, 4, 1, 1) + 0.1))
ht.table <- plot.interaction.ARMST(ht.matrix, ht.vector, ylab='Treatment in Trial Mean',
regression=TRUE, main='HT', show.legend=TRUE,legend.pos=c(.01,.98),
legend.columns=4,lwd = 1
)
par(fig=c(0,1,0,.4),mar=(c(5, 4, 0, 1) + 0.1), new=TRUE)
ht.hc <- plot.clusters.ARMST(ht.matrix, ht.vector, xlab='Trial Mean', ylab='')
par(fig = c(0, 1, 0, 1))library(agridat)
data(pacheco.soybean)
mixed.res <- standard.sensitivity.plot(pacheco.soybean,
response = "yield",
TreatmentName = "gen",
TrialName = "env",
dual.dendrogram=TRUE,
plot.outliers=TRUE,legend.columns=3)## Warning in anova.lm(base.lm): ANOVA F-tests on an essentially perfect fit
## are unreliable
print.stdplot(mixed.res)## [1] AOV
## [1] ----------------------------------------------------
## Analysis of Variance Table
##
## Response: yield
## Df Sum Sq Mean Sq F value Pr(>F)
## env 10 35202247 3520225
## gen 17 5599202 329365
## env:gen 170 13110345 77120
## Residuals 0 0
## [1] Mixed Model
## [1] ----------------------------------------------------
## Linear mixed model fit by REML ['lmerMod']
## Formula: yield ~ env + (1 | env)
## Data: plot.dat
##
## REML criterion at convergence: 2715.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4589 -0.5756 -0.1965 0.5819 3.8459
##
## Random effects:
## Groups Name Variance Std.Dev.
## env (Intercept) 271980 521.5
## Residual 100051 316.3
## Number of obs: 198, groups: env, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1756.28 526.82 3.334
## envE10 533.11 745.04 0.716
## envE11 429.11 745.04 0.576
## envE2 14.67 745.04 0.020
## envE3 -97.11 745.04 -0.130
## envE4 -631.00 745.04 -0.847
## envE5 836.22 745.04 1.122
## envE6 644.00 745.04 0.864
## envE7 639.39 745.04 0.858
## envE8 700.50 745.04 0.940
## envE9 399.61 745.04 0.536
##
## Correlation of Fixed Effects:
## (Intr) envE10 envE11 envE2 envE3 envE4 envE5 envE6 envE7
## envE10 -0.707
## envE11 -0.707 0.500
## envE2 -0.707 0.500 0.500
## envE3 -0.707 0.500 0.500 0.500
## envE4 -0.707 0.500 0.500 0.500 0.500
## envE5 -0.707 0.500 0.500 0.500 0.500 0.500
## envE6 -0.707 0.500 0.500 0.500 0.500 0.500 0.500
## envE7 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE8 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE9 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE8
## envE10
## envE11
## envE2
## envE3
## envE4
## envE5
## envE6
## envE7
## envE8
## envE9 0.500
## [1]
## [1] Stability
## [1] ----------------------------------------------------
## Treatment Slope Intercept Mean SD b
## 1 1 1.4371854 -519.540201 2457.727 694.3969 0.43718540
## 2 2 0.7529629 493.528808 2053.364 359.1532 -0.24703714
## 3 3 0.8678092 383.704504 2181.455 416.4498 -0.13219080
## 4 4 0.8428426 426.425245 2172.455 384.2841 -0.15715741
## 5 5 0.8954853 -118.720032 1736.364 466.4005 -0.10451473
## 6 6 0.9302448 246.726824 2173.818 421.7411 -0.06975521
## 7 7 1.0134538 -239.466888 1860.000 496.6359 0.01345384
## 8 8 0.9013210 -9.082115 1858.091 405.0497 -0.09867896
## 9 9 0.9022891 184.003401 2053.182 514.3938 -0.09771092
## 10 10 1.3039264 -475.481353 2225.727 668.1058 0.30392638
## 11 11 1.2222482 -273.640708 2258.364 582.7875 0.22224816
## 12 12 1.6135014 -1260.977590 2081.545 883.7377 0.61350143
## 13 13 0.7168354 600.461273 2085.455 385.8571 -0.28316462
## 14 14 0.6847354 502.414077 1920.909 389.6428 -0.31526464
## 15 15 0.9749964 139.746846 2159.545 493.3809 -0.02500360
## 16 16 0.8326711 392.405551 2117.364 423.7561 -0.16732890
## 17 17 1.1997387 -562.646605 1922.727 581.8160 0.19973872
## 18 18 0.9077530 90.138965 1970.636 446.1592 -0.09224702
## Pb bR2
## 1 0.06796959 0.3232929699
## 2 0.03765971 0.3971957795
## 3 0.30632842 0.1155826760
## 4 0.05274230 0.3557873414
## 5 0.58732928 0.0339946462
## 6 0.34517872 0.0993177085
## 7 0.93534335 0.0007726187
## 8 0.10254631 0.2684395841
## 9 0.69896421 0.0174093261
## 10 0.26263331 0.1369371880
## 11 0.20902670 0.1690456689
## 12 0.15294373 0.2130733625
## 13 0.12186434 0.2447356318
## 14 0.12224680 0.2443021526
## 15 0.89303284 0.0021213177
## 16 0.31735312 0.1107330241
## 17 0.29586138 0.1203746658
## 18 0.54524179 0.0420556074
## [1]
## [1] Tukey's 1 d.f.
## [1] ----------------------------------------------------
##
## Call:
## lm(formula = as.formula(modelString), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -646.71 -134.68 -18.94 139.41 859.00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.142e+03 1.034e+02 20.727 < 2e-16 ***
## genG10 -4.044e+02 1.172e+02 -3.450 0.000708 ***
## genG11 -2.763e+02 1.172e+02 -2.357 0.019556 *
## genG12 -2.853e+02 1.172e+02 -2.434 0.015972 *
## genG13 -7.214e+02 1.172e+02 -6.155 5.28e-09 ***
## genG14 -2.839e+02 1.172e+02 -2.422 0.016475 *
## genG15 -5.977e+02 1.172e+02 -5.100 9.04e-07 ***
## genG16 -5.996e+02 1.172e+02 -5.116 8.39e-07 ***
## genG17 -4.045e+02 1.172e+02 -3.452 0.000704 ***
## genG18 -2.320e+02 1.172e+02 -1.980 0.049383 *
## genG2 -1.994e+02 1.172e+02 -1.701 0.090775 .
## genG3 -3.762e+02 1.172e+02 -3.210 0.001590 **
## genG4 -3.723e+02 1.172e+02 -3.176 0.001773 **
## genG5 -5.368e+02 1.172e+02 -4.580 8.98e-06 ***
## genG6 -2.982e+02 1.172e+02 -2.544 0.011849 *
## genG7 -3.404e+02 1.172e+02 -2.904 0.004175 **
## genG8 -5.350e+02 1.172e+02 -4.565 9.59e-06 ***
## genG9 -4.871e+02 1.172e+02 -4.156 5.14e-05 ***
## envE10 5.331e+02 9.162e+01 5.819 2.90e-08 ***
## envE11 4.291e+02 9.162e+01 4.684 5.77e-06 ***
## envE2 1.467e+01 9.162e+01 0.160 0.873009
## envE3 -9.711e+01 9.162e+01 -1.060 0.290689
## envE4 -6.310e+02 9.162e+01 -6.887 1.07e-10 ***
## envE5 8.362e+02 9.162e+01 9.127 < 2e-16 ***
## envE6 6.440e+02 9.162e+01 7.029 4.90e-11 ***
## envE7 6.394e+02 9.162e+01 6.979 6.47e-11 ***
## envE8 7.005e+02 9.162e+01 7.646 1.49e-12 ***
## envE9 3.996e+02 9.162e+01 4.362 2.24e-05 ***
## egen:eenv 5.867e-04 2.755e-04 2.130 0.034651 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 274.9 on 169 degrees of freedom
## Multiple R-squared: 0.7632, Adjusted R-squared: 0.7239
## F-statistic: 19.45 on 28 and 169 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 17 5599202 329365 4.360 2.2e-07 ***
## env 10 35202247 3520225 46.596 < 2e-16 ***
## egen:eenv 1 342634 342634 4.535 0.0347 *
## Residuals 169 12767711 75549
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1]
## [1] Heterogenous Slopes
## [1] ----------------------------------------------------
##
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -701.95 -113.36 -18.73 113.68 832.25
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.171e+03 1.164e+02 18.649 < 2e-16 ***
## genG10 -4.044e+02 1.141e+02 -3.545 0.000522 ***
## genG11 -2.763e+02 1.141e+02 -2.422 0.016611 *
## genG12 -2.853e+02 1.141e+02 -2.501 0.013447 *
## genG13 -7.214e+02 1.141e+02 -6.324 2.67e-09 ***
## genG14 -2.839e+02 1.141e+02 -2.489 0.013889 *
## genG15 -5.977e+02 1.141e+02 -5.240 5.25e-07 ***
## genG16 -5.996e+02 1.141e+02 -5.256 4.86e-07 ***
## genG17 -4.045e+02 1.141e+02 -3.546 0.000519 ***
## genG18 -2.320e+02 1.141e+02 -2.034 0.043706 *
## genG2 -1.994e+02 1.141e+02 -1.748 0.082531 .
## genG3 -3.762e+02 1.141e+02 -3.298 0.001212 **
## genG4 -3.723e+02 1.141e+02 -3.263 0.001358 **
## genG5 -5.368e+02 1.141e+02 -4.706 5.62e-06 ***
## genG6 -2.982e+02 1.141e+02 -2.614 0.009846 **
## genG7 -3.404e+02 1.141e+02 -2.984 0.003316 **
## genG8 -5.350e+02 1.141e+02 -4.690 6.01e-06 ***
## genG9 -4.871e+02 1.141e+02 -4.270 3.42e-05 ***
## envE10 4.839e+02 1.333e+02 3.630 0.000386 ***
## envE11 3.895e+02 1.197e+02 3.255 0.001394 **
## envE2 1.331e+01 8.922e+01 0.149 0.881572
## envE3 -8.815e+01 9.099e+01 -0.969 0.334144
## envE4 -5.728e+02 1.474e+02 -3.887 0.000151 ***
## envE5 7.591e+02 1.792e+02 4.235 3.93e-05 ***
## envE6 5.846e+02 1.493e+02 3.916 0.000135 ***
## envE7 5.804e+02 1.486e+02 3.906 0.000141 ***
## envE8 6.359e+02 1.578e+02 4.029 8.81e-05 ***
## envE9 3.627e+02 1.161e+02 3.125 0.002126 **
## genG1:eenv 5.294e-01 2.705e-01 1.957 0.052178 .
## genG10:eenv -1.548e-01 2.705e-01 -0.572 0.568066
## genG11:eenv -3.994e-02 2.705e-01 -0.148 0.882820
## genG12:eenv -6.491e-02 2.705e-01 -0.240 0.810710
## genG13:eenv -1.227e-02 2.705e-01 -0.045 0.963892
## genG14:eenv 2.249e-02 2.705e-01 0.083 0.933853
## genG15:eenv 1.057e-01 2.705e-01 0.391 0.696565
## genG16:eenv -6.432e-03 2.705e-01 -0.024 0.981064
## genG17:eenv -5.464e-03 2.705e-01 -0.020 0.983913
## genG18:eenv 3.962e-01 2.705e-01 1.464 0.145149
## genG2:eenv 3.145e-01 2.705e-01 1.162 0.246862
## genG3:eenv 7.057e-01 2.705e-01 2.609 0.009992 **
## genG4:eenv -1.909e-01 2.705e-01 -0.706 0.481462
## genG5:eenv -2.230e-01 2.705e-01 -0.824 0.411037
## genG6:eenv 6.724e-02 2.705e-01 0.249 0.804044
## genG7:eenv -7.508e-02 2.705e-01 -0.278 0.781755
## genG8:eenv 2.920e-01 2.705e-01 1.079 0.282175
## genG9:eenv NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 267.5 on 153 degrees of freedom
## Multiple R-squared: 0.7969, Adjusted R-squared: 0.7385
## F-statistic: 13.64 on 44 and 153 DF, p-value: < 2.2e-16
##
##
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -701.95 -113.36 -18.73 113.68 832.25
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## genG1 2457.7273 78.3908 31.352 < 2e-16 ***
## genG10 2053.3636 78.3908 26.194 < 2e-16 ***
## genG11 2181.4545 78.3908 27.828 < 2e-16 ***
## genG12 2172.4545 78.3908 27.713 < 2e-16 ***
## genG13 1736.3636 78.3908 22.150 < 2e-16 ***
## genG14 2173.8182 78.3908 27.731 < 2e-16 ***
## genG15 1860.0000 78.3908 23.727 < 2e-16 ***
## genG16 1858.0909 78.3908 23.703 < 2e-16 ***
## genG17 2053.1818 78.3908 26.192 < 2e-16 ***
## genG18 2225.7273 78.3908 28.393 < 2e-16 ***
## genG2 2258.3636 78.3908 28.809 < 2e-16 ***
## genG3 2081.5455 78.3908 26.553 < 2e-16 ***
## genG4 2085.4545 78.3908 26.603 < 2e-16 ***
## genG5 1920.9091 78.3908 24.504 < 2e-16 ***
## genG6 2159.5455 78.3908 27.548 < 2e-16 ***
## genG7 2117.3636 78.3908 27.010 < 2e-16 ***
## genG8 1922.7273 78.3908 24.527 < 2e-16 ***
## genG9 1970.6364 78.3908 25.139 < 2e-16 ***
## genG1:eenv 1.4372 0.1859 7.730 1.07e-12 ***
## genG10:eenv 0.7530 0.1859 4.050 7.92e-05 ***
## genG11:eenv 0.8678 0.1859 4.668 6.35e-06 ***
## genG12:eenv 0.8428 0.1859 4.534 1.12e-05 ***
## genG13:eenv 0.8955 0.1859 4.817 3.33e-06 ***
## genG14:eenv 0.9302 0.1859 5.004 1.45e-06 ***
## genG15:eenv 1.0135 0.1859 5.451 1.83e-07 ***
## genG16:eenv 0.9013 0.1859 4.848 2.90e-06 ***
## genG17:eenv 0.9023 0.1859 4.853 2.84e-06 ***
## genG18:eenv 1.3039 0.1859 7.014 5.98e-11 ***
## genG2:eenv 1.2222 0.1859 6.574 6.40e-10 ***
## genG3:eenv 1.6135 0.1859 8.679 4.06e-15 ***
## genG4:eenv 0.7168 0.1859 3.856 0.000166 ***
## genG5:eenv 0.6847 0.1859 3.683 0.000314 ***
## genG6:eenv 0.9750 0.1859 5.244 4.84e-07 ***
## genG7:eenv 0.8327 0.1859 4.479 1.41e-05 ***
## genG8:eenv 1.1997 0.1859 6.453 1.21e-09 ***
## genG9:eenv 0.9078 0.1859 4.883 2.49e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 260 on 162 degrees of freedom
## Multiple R-squared: 0.9879, Adjusted R-squared: 0.9852
## F-statistic: 366.8 on 36 and 162 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 18 855318146 47517675 702.96 <2e-16 ***
## gen:eenv 18 37361982 2075666 30.71 <2e-16 ***
## Residuals 162 10950610 67596
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 17 5599202 329365 4.602 9.83e-08 ***
## env 10 35202247 3520225 49.184 < 2e-16 ***
## gen:eenv 17 2159734 127043 1.775 0.0359 *
## Residuals 153 10950610 71573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1]
## [1] Random Outliers
## [1] ----------------------------------------------------
## [1] Critical Value:
## [1] 835.5741
## [1] Interaction sd Value:
## [1] 278.5247
## [1] Error sd Value:
## [1] NaN
## [1] Pairs:
## [[1]]
## [1] 12 3
##
## [1]
## [1]data(cornelius.maize)
mixed.res <- standard.sensitivity.plot(cornelius.maize,
response = "yield",
TreatmentName = "gen",
TrialName = "env",
dual.dendrogram=TRUE,
plot.outliers=TRUE,legend.columns=3)## Warning in anova.lm(base.lm): ANOVA F-tests on an essentially perfect fit
## are unreliable
print.stdplot(mixed.res)## [1] AOV
## [1] ----------------------------------------------------
## Analysis of Variance Table
##
## Response: yield
## Df Sum Sq Mean Sq F value Pr(>F)
## env 19 247399973 13021051
## gen 8 19960404 2495051
## env:gen 152 62420142 410659
## Residuals 0 0
## [1] Mixed Model
## [1] ----------------------------------------------------
## Linear mixed model fit by REML ['lmerMod']
## Formula: yield ~ env + (1 | env)
## Data: plot.dat
##
## REML criterion at convergence: 2602.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.08736 -0.61738 -0.00426 0.64595 2.76388
##
## Random effects:
## Groups Name Variance Std.Dev.
## env (Intercept) 2226600 1492.2
## Residual 514878 717.6
## Number of obs: 180, groups: env, 20
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3612.7 1511.2 2.391
## envE02 596.8 2137.2 0.279
## envE03 1492.4 2137.2 0.698
## envE04 1617.4 2137.2 0.757
## envE05 1342.4 2137.2 0.628
## envE06 2692.8 2137.2 1.260
## envE07 -383.2 2137.2 -0.179
## envE08 415.1 2137.2 0.194
## envE09 1358.0 2137.2 0.635
## envE10 -676.2 2137.2 -0.316
## envE11 1694.6 2137.2 0.793
## envE12 3904.0 2137.2 1.827
## envE13 2719.7 2137.2 1.272
## envE14 2439.4 2137.2 1.141
## envE15 1436.2 2137.2 0.672
## envE16 1793.3 2137.2 0.839
## envE17 1264.1 2137.2 0.592
## envE18 938.8 2137.2 0.439
## envE19 -960.4 2137.2 -0.449
## envE20 1224.8 2137.2 0.573
##
## Correlation of Fixed Effects:
## (Intr) envE02 envE03 envE04 envE05 envE06 envE07 envE08 envE09
## envE02 -0.707
## envE03 -0.707 0.500
## envE04 -0.707 0.500 0.500
## envE05 -0.707 0.500 0.500 0.500
## envE06 -0.707 0.500 0.500 0.500 0.500
## envE07 -0.707 0.500 0.500 0.500 0.500 0.500
## envE08 -0.707 0.500 0.500 0.500 0.500 0.500 0.500
## envE09 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE10 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE11 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE12 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE13 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE14 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE15 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE16 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE17 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE18 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE19 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE20 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE10 envE11 envE12 envE13 envE14 envE15 envE16 envE17 envE18
## envE02
## envE03
## envE04
## envE05
## envE06
## envE07
## envE08
## envE09
## envE10
## envE11 0.500
## envE12 0.500 0.500
## envE13 0.500 0.500 0.500
## envE14 0.500 0.500 0.500 0.500
## envE15 0.500 0.500 0.500 0.500 0.500
## envE16 0.500 0.500 0.500 0.500 0.500 0.500
## envE17 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE18 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE19 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE20 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## envE19
## envE02
## envE03
## envE04
## envE05
## envE06
## envE07
## envE08
## envE09
## envE10
## envE11
## envE12
## envE13
## envE14
## envE15
## envE16
## envE17
## envE18
## envE19
## envE20 0.500
## [1]
## [1] Stability
## [1] ----------------------------------------------------
## Treatment Slope Intercept Mean SD b
## 1 1 0.9814750 -151.06934 4617.10 1362.2027 -0.01852496
## 2 2 0.9649816 -84.74141 4603.30 1254.0401 -0.03501841
## 3 3 0.9540153 188.08481 4822.85 1206.9871 -0.04598473
## 4 4 1.3142584 -1166.98659 5217.90 1707.5318 0.31425845
## 5 5 1.2280664 -719.10125 5247.05 1565.8725 0.22806640
## 6 6 1.0574596 192.88517 5330.20 1348.6694 0.05745957
## 7 7 0.8575980 506.04604 4672.40 1153.8584 -0.14240201
## 8 8 0.5240001 1737.47020 4283.15 882.0533 -0.47599990
## 9 9 1.1181456 -502.58763 4929.55 1457.8590 0.11814559
## Pb bR2
## 1 0.8909193724 0.001073705
## 2 0.7110067554 0.007810197
## 3 0.5384706179 0.021380030
## 4 0.0230407710 0.255349942
## 5 0.0379595028 0.217969243
## 6 0.5214449913 0.023203364
## 7 0.1768890353 0.098897030
## 8 0.0009673339 0.462629361
## 9 0.2980285266 0.059977186
## [1]
## [1] Tukey's 1 d.f.
## [1] ----------------------------------------------------
##
## Call:
## lm(formula = as.formula(modelString), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1706.15 -298.66 -8.26 359.07 2113.48
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.372e+03 2.359e+02 14.293 < 2e-16 ***
## genG2 -1.380e+01 1.891e+02 -0.073 0.941930
## genG3 2.058e+02 1.891e+02 1.088 0.278384
## genG4 6.008e+02 1.891e+02 3.177 0.001807 **
## genG5 6.300e+02 1.891e+02 3.331 0.001089 **
## genG6 7.131e+02 1.891e+02 3.770 0.000233 ***
## genG7 5.530e+01 1.891e+02 0.292 0.770389
## genG8 -3.339e+02 1.891e+02 -1.766 0.079465 .
## genG9 3.124e+02 1.891e+02 1.652 0.100605
## envE02 5.968e+02 2.819e+02 2.117 0.035925 *
## envE03 1.492e+03 2.819e+02 5.294 4.16e-07 ***
## envE04 1.617e+03 2.819e+02 5.737 5.10e-08 ***
## envE05 1.342e+03 2.819e+02 4.761 4.47e-06 ***
## envE06 2.693e+03 2.819e+02 9.551 < 2e-16 ***
## envE07 -3.832e+02 2.819e+02 -1.359 0.176098
## envE08 4.151e+02 2.819e+02 1.472 0.143009
## envE09 1.358e+03 2.819e+02 4.817 3.52e-06 ***
## envE10 -6.762e+02 2.819e+02 -2.398 0.017684 *
## envE11 1.695e+03 2.819e+02 6.010 1.33e-08 ***
## envE12 3.904e+03 2.819e+02 13.847 < 2e-16 ***
## envE13 2.720e+03 2.819e+02 9.646 < 2e-16 ***
## envE14 2.439e+03 2.819e+02 8.652 6.97e-15 ***
## envE15 1.436e+03 2.819e+02 5.094 1.03e-06 ***
## envE16 1.793e+03 2.819e+02 6.361 2.27e-09 ***
## envE17 1.264e+03 2.819e+02 4.484 1.44e-05 ***
## envE18 9.388e+02 2.819e+02 3.330 0.001093 **
## envE19 -9.604e+02 2.819e+02 -3.407 0.000843 ***
## envE20 1.225e+03 2.819e+02 4.344 2.55e-05 ***
## egen:eenv 5.536e-04 1.142e-04 4.848 3.07e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 598.1 on 151 degrees of freedom
## Multiple R-squared: 0.8362, Adjusted R-squared: 0.8058
## F-statistic: 27.53 on 28 and 151 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 8 19960404 2495051 6.975 8.28e-08 ***
## env 19 247399973 13021051 36.402 < 2e-16 ***
## egen:eenv 1 8406983 8406983 23.503 3.07e-06 ***
## Residuals 151 54013159 357703
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1]
## [1] Heterogenous Slopes
## [1] ----------------------------------------------------
##
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1610.55 -334.46 15.47 324.12 2208.91
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.224e+03 2.697e+02 11.956 < 2e-16 ***
## genG2 -1.380e+01 1.880e+02 -0.073 0.941597
## genG3 2.058e+02 1.880e+02 1.094 0.275688
## genG4 6.008e+02 1.880e+02 3.195 0.001717 **
## genG5 6.300e+02 1.880e+02 3.350 0.001031 **
## genG6 7.131e+02 1.880e+02 3.792 0.000219 ***
## genG7 5.530e+01 1.880e+02 0.294 0.769109
## genG8 -3.339e+02 1.880e+02 -1.776 0.077844 .
## genG9 3.124e+02 1.880e+02 1.662 0.098756 .
## envE02 6.673e+02 2.875e+02 2.321 0.021681 *
## envE03 1.669e+03 3.225e+02 5.174 7.56e-07 ***
## envE04 1.809e+03 3.294e+02 5.491 1.76e-07 ***
## envE05 1.501e+03 3.149e+02 4.766 4.54e-06 ***
## envE06 3.011e+03 4.018e+02 7.493 6.26e-12 ***
## envE07 -4.285e+02 2.833e+02 -1.513 0.132571
## envE08 4.642e+02 2.838e+02 1.636 0.104126
## envE09 1.518e+03 3.157e+02 4.810 3.76e-06 ***
## envE10 -7.561e+02 2.895e+02 -2.612 0.009956 **
## envE11 1.895e+03 3.338e+02 5.677 7.30e-08 ***
## envE12 4.365e+03 5.028e+02 8.682 7.69e-15 ***
## envE13 3.041e+03 4.039e+02 7.529 5.13e-12 ***
## envE14 2.728e+03 3.829e+02 7.124 4.63e-11 ***
## envE15 1.606e+03 3.196e+02 5.024 1.47e-06 ***
## envE16 2.005e+03 3.396e+02 5.904 2.44e-08 ***
## envE17 1.413e+03 3.112e+02 4.542 1.17e-05 ***
## envE18 1.050e+03 2.977e+02 3.526 0.000567 ***
## envE19 -1.074e+03 2.985e+02 -3.597 0.000441 ***
## envE20 1.369e+03 3.094e+02 4.426 1.88e-05 ***
## genG1:eenv -1.367e-01 1.604e-01 -0.852 0.395562
## genG2:eenv -1.532e-01 1.604e-01 -0.955 0.341201
## genG3:eenv -1.641e-01 1.604e-01 -1.023 0.307869
## genG4:eenv 1.961e-01 1.604e-01 1.223 0.223428
## genG5:eenv 1.099e-01 1.604e-01 0.685 0.494232
## genG6:eenv -6.069e-02 1.604e-01 -0.378 0.705714
## genG7:eenv -2.606e-01 1.604e-01 -1.624 0.106461
## genG8:eenv -5.941e-01 1.604e-01 -3.704 0.000301 ***
## genG9:eenv NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 594.6 on 144 degrees of freedom
## Multiple R-squared: 0.8456, Adjusted R-squared: 0.8081
## F-statistic: 22.53 on 35 and 144 DF, p-value: < 2.2e-16
##
##
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1610.55 -334.46 15.47 324.12 2208.91
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## genG1 4617.1000 125.3564 36.832 < 2e-16 ***
## genG2 4603.3000 125.3564 36.722 < 2e-16 ***
## genG3 4822.8500 125.3564 38.473 < 2e-16 ***
## genG4 5217.9000 125.3564 41.625 < 2e-16 ***
## genG5 5247.0500 125.3564 41.857 < 2e-16 ***
## genG6 5330.2000 125.3564 42.520 < 2e-16 ***
## genG7 4672.4000 125.3564 37.273 < 2e-16 ***
## genG8 4283.1500 125.3564 34.168 < 2e-16 ***
## genG9 4929.5500 125.3564 39.324 < 2e-16 ***
## genG1:eenv 0.9815 0.1069 9.179 < 2e-16 ***
## genG2:eenv 0.9650 0.1069 9.025 5.03e-16 ***
## genG3:eenv 0.9540 0.1069 8.922 9.36e-16 ***
## genG4:eenv 1.3143 0.1069 12.291 < 2e-16 ***
## genG5:eenv 1.2281 0.1069 11.485 < 2e-16 ***
## genG6:eenv 1.0575 0.1069 9.890 < 2e-16 ***
## genG7:eenv 0.8576 0.1069 8.020 2.00e-13 ***
## genG8:eenv 0.5240 0.1069 4.901 2.30e-06 ***
## genG9:eenv 1.1181 0.1069 10.457 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 560.6 on 162 degrees of freedom
## Multiple R-squared: 0.9889, Adjusted R-squared: 0.9876
## F-statistic: 800.3 on 18 and 162 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 9 4.268e+09 474253490 1508.99 <2e-16 ***
## gen:eenv 9 2.589e+08 28767336 91.53 <2e-16 ***
## Residuals 162 5.091e+07 314285
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 8 19960404 2495051 7.057 7.62e-08 ***
## env 19 247399973 13021051 36.827 < 2e-16 ***
## gen:eenv 8 11506049 1438256 4.068 0.000217 ***
## Residuals 144 50914093 353570
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1]
## [1] Random Outliers
## [1] ----------------------------------------------------
## [1] Critical Value:
## [1] 1928.835
## [1] Interaction sd Value:
## [1] 642.9451
## [1] Error sd Value:
## [1] NaN
## [1] Pairs:
## [[1]]
## [1] 1 8
##
## [1]
## [1]tdf.tbl <- anova(mixed.res$tdf$multiplicative.lm)
anova(mixed.res$tdf$multiplicative.lm)## Analysis of Variance Table
##
## Response: yield
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 8 19960404 2495051 6.9752 8.283e-08 ***
## env 19 247399973 13021051 36.4018 < 2.2e-16 ***
## egen:eenv 1 8406983 8406983 23.5027 3.070e-06 ***
## Residuals 151 54013159 357703
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mixed.res$tdf$multiplicative.lm)##
## Call:
## lm(formula = as.formula(modelString), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1706.15 -298.66 -8.26 359.07 2113.48
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.372e+03 2.359e+02 14.293 < 2e-16 ***
## genG2 -1.380e+01 1.891e+02 -0.073 0.941930
## genG3 2.058e+02 1.891e+02 1.088 0.278384
## genG4 6.008e+02 1.891e+02 3.177 0.001807 **
## genG5 6.300e+02 1.891e+02 3.331 0.001089 **
## genG6 7.131e+02 1.891e+02 3.770 0.000233 ***
## genG7 5.530e+01 1.891e+02 0.292 0.770389
## genG8 -3.339e+02 1.891e+02 -1.766 0.079465 .
## genG9 3.124e+02 1.891e+02 1.652 0.100605
## envE02 5.968e+02 2.819e+02 2.117 0.035925 *
## envE03 1.492e+03 2.819e+02 5.294 4.16e-07 ***
## envE04 1.617e+03 2.819e+02 5.737 5.10e-08 ***
## envE05 1.342e+03 2.819e+02 4.761 4.47e-06 ***
## envE06 2.693e+03 2.819e+02 9.551 < 2e-16 ***
## envE07 -3.832e+02 2.819e+02 -1.359 0.176098
## envE08 4.151e+02 2.819e+02 1.472 0.143009
## envE09 1.358e+03 2.819e+02 4.817 3.52e-06 ***
## envE10 -6.762e+02 2.819e+02 -2.398 0.017684 *
## envE11 1.695e+03 2.819e+02 6.010 1.33e-08 ***
## envE12 3.904e+03 2.819e+02 13.847 < 2e-16 ***
## envE13 2.720e+03 2.819e+02 9.646 < 2e-16 ***
## envE14 2.439e+03 2.819e+02 8.652 6.97e-15 ***
## envE15 1.436e+03 2.819e+02 5.094 1.03e-06 ***
## envE16 1.793e+03 2.819e+02 6.361 2.27e-09 ***
## envE17 1.264e+03 2.819e+02 4.484 1.44e-05 ***
## envE18 9.388e+02 2.819e+02 3.330 0.001093 **
## envE19 -9.604e+02 2.819e+02 -3.407 0.000843 ***
## envE20 1.225e+03 2.819e+02 4.344 2.55e-05 ***
## egen:eenv 5.536e-04 1.142e-04 4.848 3.07e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 598.1 on 151 degrees of freedom
## Multiple R-squared: 0.8362, Adjusted R-squared: 0.8058
## F-statistic: 27.53 on 28 and 151 DF, p-value: < 2.2e-16Working backwards, standard error for genotype estimates, the heterogeneous null model, is 560.6/sqrt(20), but I still haven’t worked out the SE for heterogeneous slopes. Still, if we assume balanced, then we can use the given standard error
sqrt(357703)/sqrt(8406983)
ee <- mixed.res\(tdf\)multiplicative.lm\(model\)egenmixed.res\(tdf\)multiplicative.lm\(model\)eenv > sum(eeee) [1] 2.743446e+13 > sqrt(357703/2.743446e+13) [1] 0.0001141861
SE = sb1 = sqrt [ Σ(yi - ŷi)2 / (n - 2) ] / sqrt [ Σ(xi - x)2 ]